title

Deep Learning Interview Prep Course

description

Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations.
✏️ Course created by Tatev Karen Aslanyan.
✏️ Expanded course with 100 questions: https://courses.lunartech.ai/courses/deep-learning-interview-preparation-course-100-q-a-s
⭐️ Contents ⭐️
⌨️ 0:00:00 Introduction
⌨️ 0:08:20 Question 1: What is Deep Learning?
⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning?
⌨️ 0:15:25 Question 3: What is a Neural Network?
⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning
⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way
⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network?
⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them
⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network?
⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works
⌨️ 0:53:45 Question 10: What is Gradient Descent?
⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning?
⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning?
⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent?
⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN
⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN
⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason?
⌨️ 1:37:50 Question 17: What do you understand by a computational graph?
⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning?
⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry?
⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems?
⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks?
⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima?
⌨️ 2:03:38 Question 23: How is GD different from SGD?
⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term?
⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent.
⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)?
⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model?
⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages?
⌨️ 2:34:12 Question 29: What is RMSProp and how does it work?
⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods
⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs?
⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam?
⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN?
⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN?
⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN?
⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN?
⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN?
⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients?
⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients?
⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights?
⌨️ 3:29:18 Question 41: What is Dropout and how does it work?
⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN?
⌨️ 3:35:06 Question 43: Is Dropout like Random Forest?
⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing?
⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN?
⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN?
⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN?
⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI?
⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality?
⌨️ 3:56:47 Question 50: What are Generative Models, give examples?

detail

{'title': 'Deep Learning Interview Prep Course', 'heatmap': [], 'summary': 'This deep learning interview prep course covers fundamental topics including neural networks, optimization algorithms, activation functions, and vanishing gradient problems. it provides insights into stabilization techniques, optimization algorithms, and model parameter optimization, addressing overfitting, vanishing and exploding gradient problems, and the curse of dimensionality.', 'chapters': [{'end': 473.048, 'segs': [{'end': 28.43, 'src': 'embed', 'start': 0.069, 'weight': 1, 'content': [{'end': 4.011, 'text': 'Prepare for a job interview about deep learning with this course from Tadev.', 'start': 0.069, 'duration': 3.942}, {'end': 8.196, 'text': 'She goes over 50 common interview questions related to deep learning.', 'start': 4.573, 'duration': 3.623}, {'end': 14.321, 'text': "She's an experienced data science professional and has published papers in machine learning scientific journals.", 'start': 8.656, 'duration': 5.665}, {'end': 19.265, 'text': "I'm Tadev from LearnerTech.", 'start': 14.341, 'duration': 4.924}, {'end': 24.55, 'text': "In this course, I'm going to teach you everything that you need to know from the topic of deep learning.", 'start': 19.566, 'duration': 4.984}, {'end': 28.43, 'text': "I'm going to help you prepare for your deep learning interviews.", 'start': 25.208, 'duration': 3.222}], 'summary': "Tadev's course covers 50 deep learning interview questions for job preparation.", 'duration': 28.361, 'max_score': 0.069, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY69.jpg'}, {'end': 71.521, 'src': 'embed', 'start': 45.06, 'weight': 0, 'content': [{'end': 52.386, 'text': 'Deep learning is one of the most popular topics these days, because it forms the cornerstone of topics such as large language models,', 'start': 45.06, 'duration': 7.326}, {'end': 54.448, 'text': 'but also the generative AI.', 'start': 52.386, 'duration': 2.062}, {'end': 56.71, 'text': 'It contains many complex concepts.', 'start': 54.748, 'duration': 1.962}, {'end': 66.438, 'text': 'It combines linear algebra, mathematics, differentiation theory and also advanced algorithms and they all come together to form this field of AI.', 'start': 57.271, 'duration': 9.167}, {'end': 71.521, 'text': 'which is the fundamental part of generative AI, the large language models.', 'start': 67.259, 'duration': 4.262}], 'summary': 'Deep learning is fundamental to generative ai and large language models.', 'duration': 26.461, 'max_score': 45.06, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY45060.jpg'}, {'end': 216.516, 'src': 'embed', 'start': 187.582, 'weight': 3, 'content': [{'end': 192.704, 'text': "So we'll be covering the SGD algorithm, so more advanced version of GD.", 'start': 187.582, 'duration': 5.122}, {'end': 195.404, 'text': 'We will be covering the SGD with momentum.', 'start': 193.184, 'duration': 2.22}, {'end': 200.706, 'text': "What is the role of this momentum? We'll also be covering the different versions and variants of GD.", 'start': 195.444, 'duration': 5.262}, {'end': 210.052, 'text': 'something that comes time and time again during the deep learning interviews, such as the BatchGD, MiniBatchGD, and the SGD.', 'start': 201.506, 'duration': 8.546}, {'end': 216.516, 'text': 'What is the difference between them? What is the function of batch size? Another very popular question you can expect.', 'start': 210.552, 'duration': 5.964}], 'summary': 'The transcript covers advanced versions of gd algorithm, including sgd with momentum and different variants like batchgd, minibatchgd, and the sgd.', 'duration': 28.934, 'max_score': 187.582, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY187582.jpg'}, {'end': 286.121, 'src': 'embed', 'start': 261.212, 'weight': 4, 'content': [{'end': 266.574, 'text': 'as well as the ways that we can solve vanishing gradient problem and the exploding gradient problem.', 'start': 261.212, 'duration': 5.362}, {'end': 275.112, 'text': 'Then we will also be covering the concept of overfitting, definitely something that you can expect during your deep learning interviews.', 'start': 267.546, 'duration': 7.566}, {'end': 286.121, 'text': "Then as part of the final 10 questions of this part of the course, we'll be covering the concept of dropout and the regularization in neural networks.", 'start': 276.013, 'duration': 10.108}], 'summary': 'Covering vanishing/exploding gradient, overfitting, dropout, regularization in neural networks.', 'duration': 24.909, 'max_score': 261.212, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY261212.jpg'}], 'start': 0.069, 'title': 'Deep learning fundamentals', 'summary': 'Provides a comprehensive overview of deep learning, covering basic concepts to complex topics including neural networks, optimization algorithms, and overfitting, and includes follow-up interview preparations. it addresses 50 common interview questions crucial for data science, machine learning, ai, and research scientist interviews.', 'chapters': [{'end': 83.526, 'start': 0.069, 'title': 'Deep learning interview prep', 'summary': 'Covers a deep learning interview preparation course by tadev from learnertech, addressing 50 common interview questions, crucial for data science, 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['Comprehensive coverage of deep learning concepts, including basic and advanced topics, with a focus on preparing for deep learning interviews and follow-up questions.', 'Detailed explanation of various optimization algorithms, including SGD, momentum, BatchGD, MiniBatchGD, Haitian, RMS prop, and Adam algorithm, crucial for deep learning interviews.', 'In-depth understanding of key concepts like vanishing gradient problem, exploding gradient problem, batch normalization, layer normalization, residual connections, gradient clipping, and overfitting.', 'Insightful coverage of regularization techniques such as dropout, L1 regularization, and L2 regularization, along with their impact on weights and performance.', 'Emphasis on the importance of mastering deep learning fundamentals to effectively tackle more complex models like transformers, GPT, and T5, and preparing for follow-up questions during interviews.']}], 'duration': 472.979, 'thumbnail': 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residual connections, gradient clipping, and overfitting.']}, {'end': 1615.057, 'segs': [{'end': 548.082, 'src': 'embed', 'start': 515.626, 'weight': 0, 'content': [{'end': 520.928, 'text': 'So deep learning is a subset of machine learning, which is then a subset of AI.', 'start': 515.626, 'duration': 5.302}, {'end': 532.571, 'text': 'So a branch of artificial intelligence which involves training artificial neural networks on large amount of data in order to identify and learn those hidden patterns,', 'start': 521.328, 'duration': 11.243}, {'end': 534.792, 'text': 'nonlinear relationships in the data.', 'start': 532.571, 'duration': 2.221}, {'end': 538.975, 'text': 'which is something that the traditional machine learning models,', 'start': 535.592, 'duration': 3.383}, {'end': 548.082, 'text': 'like linear regression or logistic regression or random forest and XGBoost are not doing so well as the deep learning models.', 'start': 538.975, 'duration': 9.107}], 'summary': 'Deep learning is a subset of ai, involving training neural networks on large data to identify hidden patterns, outperforming traditional machine learning models.', 'duration': 32.456, 'max_score': 515.626, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY515626.jpg'}, {'end': 613.543, 'src': 'embed', 'start': 587.346, 'weight': 4, 'content': [{'end': 600.738, 'text': 'in order to take the input data in the input layer and then transform this through the activation functions to activate these neurons and then transform it into activations,', 'start': 587.346, 'duration': 13.392}, {'end': 602.599, 'text': 'and this will be in our hidden layers.', 'start': 600.738, 'duration': 1.861}, {'end': 609.342, 'text': 'And the magic happens in a deep learning model to identify the nonlinear relationships in the input data.', 'start': 603.42, 'duration': 5.922}, {'end': 613.543, 'text': 'And then what it does is that it produces then the output.', 'start': 609.962, 'duration': 3.581}], 'summary': 'Deep learning models identify nonlinear relationships in input data and produce output.', 'duration': 26.197, 'max_score': 587.346, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY587346.jpg'}, {'end': 675.467, 'src': 'embed', 'start': 651.456, 'weight': 11, 'content': [{'end': 658.758, 'text': 'examples of deep learning models are, for instance, artificial neural networks, recurrent neural networks, LSTMs,', 'start': 651.456, 'duration': 7.302}, {'end': 663.76, 'text': 'the different sorts of more advanced architectures such as variational autoencoders.', 'start': 658.758, 'duration': 5.002}, {'end': 665.661, 'text': 'they are also based on neural networks.', 'start': 663.76, 'duration': 1.901}, {'end': 667.662, 'text': 'you get the idea?', 'start': 666.141, 'duration': 1.521}, {'end': 675.467, 'text': 'so if i were to answer this question very short, i would just say that deep learning models are a subset of machine learning models,', 'start': 667.662, 'duration': 7.805}], 'summary': 'Deep learning models include artificial neural networks, recurrent neural networks, lstms, and variational autoencoders, all of which are based on neural networks and are a subset of machine learning models.', 'duration': 24.011, 'max_score': 651.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY651456.jpg'}, {'end': 743.579, 'src': 'embed', 'start': 719.92, 'weight': 6, 'content': [{'end': 733.77, 'text': 'So do mention a few examples of traditional machine learning models and then mention a few examples of deep learning models and try to put the focus on one or two advantages of the deep learning models.', 'start': 719.92, 'duration': 13.85}, {'end': 743.579, 'text': 'So a few examples of machine learning models are linear regression, logistic regression, the support vector machines, naive variation algorithm,', 'start': 734.711, 'duration': 8.868}], 'summary': 'Traditional ml models: linear regression, logistic regression, svm, naive bayes. deep learning models: (not mentioned). focus on one or two advantages of deep learning models.', 'duration': 23.659, 'max_score': 719.92, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY719920.jpg'}, {'end': 812.929, 'src': 'embed', 'start': 787.785, 'weight': 9, 'content': [{'end': 797.499, 'text': 'Then Du pointed out that the first biggest difference is that traditional machine learning models, they rely on manual feature extraction.', 'start': 787.785, 'duration': 9.714}, {'end': 805.925, 'text': 'while deep learning models they do this automatically and they automatically take the input data as it is.', 'start': 798.26, 'duration': 7.665}, {'end': 812.929, 'text': "we don't need to perform feature selection we don't need to worry about that, because our model, represented by neural networks,", 'start': 805.925, 'duration': 7.004}], 'summary': 'Deep learning models automate feature extraction, eliminating the need for manual selection.', 'duration': 25.144, 'max_score': 787.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY787785.jpg'}, {'end': 863.281, 'src': 'embed', 'start': 833.508, 'weight': 10, 'content': [{'end': 841.452, 'text': 'so deep learning models perform very well on big data, something that we have seen machine learning models suffer.', 'start': 833.508, 'duration': 7.944}, {'end': 849.377, 'text': 'so ml models are known for becoming worse in terms of their performance when the size of the data increases.', 'start': 841.452, 'duration': 7.925}, {'end': 851.458, 'text': 'when the number of features increases,', 'start': 849.377, 'duration': 2.081}, {'end': 863.281, 'text': 'they start to overfit and they start also to become unstable and not accurately predict the output when we increase the number of observations in our data.', 'start': 851.458, 'duration': 11.823}], 'summary': 'Deep learning models outperform machine learning on big data, as ml models suffer performance degradation with increased data size and features.', 'duration': 29.773, 'max_score': 833.508, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY833508.jpg'}, {'end': 898.377, 'src': 'embed', 'start': 871.843, 'weight': 8, 'content': [{'end': 877.904, 'text': 'especially in the tests such as computer vision or speech recognition machine translation.', 'start': 871.843, 'duration': 6.061}, {'end': 880.926, 'text': 'And the idea of deep learning models.', 'start': 878.704, 'duration': 2.222}, {'end': 892.053, 'text': 'you can even see why they are better than machine learning models by even looking at the most recent different sorts of applications across different tech fields.', 'start': 880.926, 'duration': 11.127}, {'end': 898.377, 'text': 'You can see that most of them are based on some sort of neural network-based algorithm rather than machine learning models.', 'start': 892.613, 'duration': 5.764}], 'summary': 'Deep learning outperforms machine learning in various tech fields.', 'duration': 26.534, 'max_score': 871.843, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY871843.jpg'}, {'end': 1027.751, 'src': 'embed', 'start': 1002.02, 'weight': 1, 'content': [{'end': 1007.991, 'text': 'And the end result is to minimize the amount of error we are making when we are learning.', 'start': 1002.02, 'duration': 5.971}, {'end': 1011.301, 'text': "And that's exactly what we are doing in neural networks.", 'start': 1008.82, 'duration': 2.481}, {'end': 1017.905, 'text': 'So the core of neural network is made up of these units, which we are calling neurons, like in our human brains.', 'start': 1011.782, 'duration': 6.123}, {'end': 1023.528, 'text': 'And these neurons are together forming the layers.', 'start': 1018.746, 'duration': 4.782}, {'end': 1027.751, 'text': 'We have input layer, which represents our input data.', 'start': 1023.949, 'duration': 3.802}], 'summary': 'Neural networks aim to minimize learning errors by using layers of neurons to process input data.', 'duration': 25.731, 'max_score': 1002.02, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY1002020.jpg'}, {'end': 1267.773, 'src': 'embed', 'start': 1238.292, 'weight': 3, 'content': [{'end': 1242.615, 'text': 'continuously activate those neurons, get the output,', 'start': 1238.292, 'duration': 4.323}, {'end': 1250.278, 'text': 'understand what is the loss that our model is making the overall cost function and then compute the what we are calling gradients.', 'start': 1242.615, 'duration': 7.663}, {'end': 1256, 'text': 'And you briefly mentioned here the idea of gradients, but do not go too much into detail of it.', 'start': 1251.058, 'duration': 4.942}, {'end': 1267.773, 'text': 'And then, using these gradients, we understand how much we need to update these weights in order to improve our parameters of our model, because,', 'start': 1256.68, 'duration': 11.093}], 'summary': 'Activate neurons, compute gradients, update weights to improve model parameters.', 'duration': 29.481, 'max_score': 1238.292, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY1238292.jpg'}, {'end': 1353.1, 'src': 'embed', 'start': 1327.862, 'weight': 5, 'content': [{'end': 1338.527, 'text': "So a neuron in deep learning is sometimes referred to as an artificial neuron, which mimics the function of the human brain's neuron.", 'start': 1327.862, 'duration': 10.665}, {'end': 1341.748, 'text': 'But it does so in an automatic and simple way.', 'start': 1339.007, 'duration': 2.741}, {'end': 1353.1, 'text': 'And the idea is that in a neural network, The model gets different inputs and here these input signals we are calling neurons.', 'start': 1342.448, 'duration': 10.652}], 'summary': 'Deep learning neurons mimic human brain function in a simple, automatic way.', 'duration': 25.238, 'max_score': 1327.862, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY1327862.jpg'}, {'end': 1538.963, 'src': 'embed', 'start': 1505.886, 'weight': 7, 'content': [{'end': 1516.173, 'text': 'so the neural network is this multi-layer structured model, and each layer is transforming the input data step by step.', 'start': 1505.886, 'duration': 10.287}, {'end': 1518.554, 'text': 'think of it like an assembly line.', 'start': 1516.173, 'duration': 2.381}, {'end': 1525.299, 'text': 'so assembly line where every stage adds more complexity on the previous stage,', 'start': 1518.554, 'duration': 6.745}, {'end': 1534.222, 'text': 'And the detail and the complexity is adding more value to the ability of the model to perform predictions.', 'start': 1526.079, 'duration': 8.143}, {'end': 1538.963, 'text': 'So in the beginning, the model has these input layers.', 'start': 1534.522, 'duration': 4.441}], 'summary': 'Neural network: multi-layer model adding complexity to improve predictions.', 'duration': 33.077, 'max_score': 1505.886, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY1505886.jpg'}], 'start': 473.048, 'title': 'Deep learning and neural networks', 'summary': 'Covers the differences between deep learning and traditional machine learning, emphasizing deep learning as a subset of machine learning and a branch of ai. it discusses the advantages of deep learning models, including automatic feature extraction and superior performance on big data. furthermore, it explains the foundational concept of neural networks, their architecture, structure of neurons, training process, and the role of activation functions in introducing non-linearity.', 'chapters': [{'end': 764.998, 'start': 473.048, 'title': 'Deep learning for interviews', 'summary': 'Discusses the differences between deep learning and traditional machine learning, emphasizing that deep learning models are a subset of machine learning models and a branch of artificial intelligence, which aims to learn complex data and discover hidden patterns in large amounts of data for various tasks like computer vision and nlp.', 'duration': 291.95, 'highlights': ['Deep learning models are a subset of machine learning models, a branch of artificial intelligence that aims to learn complex data and discover hidden patterns in large amounts of data for various tasks like computer vision, NLP, based on neural networks. (Relevance: 5)', 'Deep learning involves training artificial neural networks on large amounts of data to identify and learn hidden patterns and nonlinear relationships, which traditional machine learning models like linear regression or random forest are not capable of. (Relevance: 4)', 'The heart of deep learning is the concept of layers and activation functions, replicating the way human brains work to take input data, transform it through layers, and identify nonlinear relationships, producing the output. (Relevance: 3)', 'Traditional machine learning models include linear regression, logistic regression, support vector machines, naive Bayes algorithm, k-means, DB-scan, GBM, XGBoost, and random forest, suitable for various types of tasks. (Relevance: 2)', 'Deep learning models include artificial neural networks, recurrent neural networks, LSTMs, and advanced architectures like variational autoencoders, all based on neural networks and are used for various tasks such as computer vision and NLP. (Relevance: 1)']}, {'end': 1001.299, 'start': 766.019, 'title': 'Deep learning models and neural networks', 'summary': 'Discusses the advantages of deep learning models over traditional machine learning models, citing their automatic feature extraction and superior performance on big data. it also explains the foundational concept of neural networks in deep learning and artificial intelligence.', 'duration': 235.28, 'highlights': ['Deep learning models perform very well on big data, outperforming traditional machine learning models, especially in tests such as computer vision, speech recognition, and machine translation.', 'Traditional machine learning models rely on manual feature extraction, while deep learning models automatically extract important features from the input data, leading to better performance in regression and classification tasks.', 'Deep learning models are able to handle large data and complex features, while traditional machine learning models suffer in terms of performance when the size of the data and number of features increase.']}, {'end': 1615.057, 'start': 1002.02, 'title': 'Neural network architecture', 'summary': 'Explains the concept of neural networks, including the structure of neurons, the process of training, and the role of activation functions in introducing non-linearity, aiming to minimize error in learning and accurately predict outcomes.', 'duration': 613.037, 'highlights': ['The core of neural network is made up of neurons forming layers, including input layer and one or more hidden layers, which help in learning and understanding different patterns and transforming information. (Relevance: 5)', "The process involves continuously activating neurons, computing the overall cost function, and using gradients to update weights and parameters to minimize error in the model's predictions. (Relevance: 4)", "A neuron in deep learning mimics the function of human brain's neuron, taking input data, multiplying it by weights, adding bias factors, and applying activation functions to introduce non-linearity and learn complex structures in input data. (Relevance: 3)", "The architecture of neural networks consists of multiple layers transforming input data step by step, resembling an assembly line adding complexity and value to the model's ability to perform predictions. (Relevance: 2)"]}], 'duration': 1142.009, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY473048.jpg', 'highlights': ['Deep learning models are a subset of machine learning models, a branch of artificial intelligence that aims to learn complex data and discover hidden patterns in large amounts of data for various tasks like computer vision, NLP, based on neural networks. (Relevance: 5)', 'The core of neural network is made up of neurons forming layers, including input layer and one or more hidden layers, which help in learning and understanding different patterns and transforming information. (Relevance: 5)', 'Deep learning involves training artificial neural networks on large amounts of data to identify and learn hidden patterns and nonlinear relationships, which traditional machine learning models like linear regression or random forest are not capable of. (Relevance: 4)', "The process involves continuously activating neurons, computing the overall cost function, and using gradients to update weights and parameters to minimize error in the model's predictions. (Relevance: 4)", 'The heart of deep learning is the concept of layers and activation functions, replicating the way human brains work to take input data, transform it through layers, and identify nonlinear relationships, producing the output. (Relevance: 3)', "A neuron in deep learning mimics the function of human brain's neuron, taking input data, multiplying it by weights, adding bias factors, and applying activation functions to introduce non-linearity and learn complex structures in input data. (Relevance: 3)", 'Traditional machine learning models include linear regression, logistic regression, support vector machines, naive Bayes algorithm, k-means, DB-scan, GBM, XGBoost, and random forest, suitable for various types of tasks. (Relevance: 2)', "The architecture of neural networks consists of multiple layers transforming input data step by step, resembling an assembly line adding complexity and value to the model's ability to perform predictions. (Relevance: 2)", 'Deep learning models perform very well on big data, outperforming traditional machine learning models, especially in tests such as computer vision, speech recognition, and machine translation.', 'Traditional machine learning models rely on manual feature extraction, while deep learning models automatically extract important features from the input data, leading to better performance in regression and classification tasks.', 'Deep learning models are able to handle large data and complex features, while traditional machine learning models suffer in terms of performance when the size of the data and number of features increase.', 'Deep learning models include artificial neural networks, recurrent neural networks, LSTMs, and advanced architectures like variational autoencoders, all based on neural networks and are used for various tasks such as computer vision and NLP.']}, {'end': 2717.573, 'segs': [{'end': 1690.91, 'src': 'embed', 'start': 1644.162, 'weight': 1, 'content': [{'end': 1652.35, 'text': 'So, in this simple architecture we have just we got just one hidden layer, but it can also be that you have more hidden layers,', 'start': 1644.162, 'duration': 8.188}, {'end': 1656.933, 'text': 'which is usually the case with the traditional deep neural networks.', 'start': 1652.35, 'duration': 4.583}, {'end': 1658.855, 'text': 'hence also the name deep.', 'start': 1657.634, 'duration': 1.221}, {'end': 1670.919, 'text': 'So what these weights do is that they help us to understand how much this input feature should contribute to first hidden unit,', 'start': 1659.215, 'duration': 11.704}, {'end': 1673.22, 'text': 'second hidden unit and third hidden unit.', 'start': 1670.919, 'duration': 2.301}, {'end': 1677.242, 'text': 'So as you can see, in our hidden layer, we have the three circles.', 'start': 1673.5, 'duration': 3.742}, {'end': 1681.844, 'text': 'These three circles describe the hidden units in our hidden layer.', 'start': 1677.762, 'duration': 4.082}, {'end': 1684.205, 'text': 'and we have this simple structure.', 'start': 1682.504, 'duration': 1.701}, {'end': 1686.827, 'text': 'so we got just three hidden units.', 'start': 1684.205, 'duration': 2.622}, {'end': 1690.91, 'text': 'but this is something that you can decide for yourself when training your model.', 'start': 1686.827, 'duration': 4.083}], 'summary': 'Simple architecture with one hidden layer, also allowing for more hidden layers, typically seen in traditional deep neural networks. in this case, there are three hidden units.', 'duration': 46.748, 'max_score': 1644.162, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY1644162.jpg'}, {'end': 1982.719, 'src': 'embed', 'start': 1955.189, 'weight': 0, 'content': [{'end': 1963.975, 'text': 'If we were to not use specific type of activation functions, our model will be similar to the linear regression model.', 'start': 1955.189, 'duration': 8.786}, {'end': 1974.097, 'text': 'we will have a plain linear type of model that will be able to uncover the linear patterns and will not be able to discover these complex,', 'start': 1964.295, 'duration': 9.802}, {'end': 1976.498, 'text': 'hidden patterns in the data.', 'start': 1974.097, 'duration': 2.401}, {'end': 1982.719, 'text': 'and we have seen that in the true world, the data most of the time contains non-linear relationships.', 'start': 1976.498, 'duration': 6.221}], 'summary': 'Without specific activation functions, model becomes similar to linear regression, fails to uncover complex, hidden patterns in non-linear data.', 'duration': 27.53, 'max_score': 1955.189, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY1955189.jpg'}, {'end': 2055.648, 'src': 'embed', 'start': 2028.217, 'weight': 3, 'content': [{'end': 2041.264, 'text': 'It basically defines how much we need to add a value that corresponds to that specific input when computing the hidden units value that we saw before.', 'start': 2028.217, 'duration': 13.047}, {'end': 2044.405, 'text': 'And there are different sorts of activation functions.', 'start': 2041.704, 'duration': 2.701}, {'end': 2053.507, 'text': 'I do mention briefly that the four popular activation functions are sigmoid activation function, the hyperbolic tank function,', 'start': 2044.425, 'duration': 9.082}, {'end': 2055.648, 'text': 'which shortly is referred as tank function.', 'start': 2053.507, 'duration': 2.141}], 'summary': 'Describes adding value to input for computing hidden units; mentions four popular activation functions.', 'duration': 27.431, 'max_score': 2028.217, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY2028217.jpg'}], 'start': 1615.057, 'title': 'Neural network fundamentals', 'summary': 'Covers the fundamentals of neural networks, including neural network architecture with a focus on a simple structure with three hidden units, and the role and impact of activation functions such as sigmoid, tanh, relu, and leaky relu on non-linearity and network performance.', 'chapters': [{'end': 1690.91, 'start': 1615.057, 'title': 'Neural network architecture', 'summary': 'Discusses the structure of a neural network, explaining the weights assigned to input features and their contribution to hidden units, with a focus on a simple architecture with three hidden units.', 'duration': 75.853, 'highlights': ['The weights assigned to input features determine their contribution to hidden units, enabling the understanding of their impact on the hidden layer.', 'The structure includes a simple architecture with three hidden units, but it can be adapted to include more hidden layers, typical in traditional deep neural networks.']}, {'end': 2717.573, 'start': 1690.91, 'title': 'Neural network activation functions', 'summary': "Explains the role of activation functions in neural networks, including the impact on non-linearity, the function of popular activation functions such as sigmoid, tanh, relu, and leaky relu, and their influence on the network's performance.", 'duration': 1026.663, 'highlights': ['The activation functions in neural networks introduce non-linearity, crucial for uncovering complex, hidden patterns in data, and avoiding a plain linear model.', 'The four popular activation functions mentioned are sigmoid, tanh, ReLU, and Leaky ReLU, each serving specific roles in transforming z-scores to activation values and handling saturation issues.', 'The sigmoid activation function produces values between 0 and 1, suffering from saturation, while the tanh function transforms values between -1 and 1, also facing saturation challenges.', 'The ReLU activation function activates positive z-scores and sets negative z-scores to zero, providing a linear representation for positive inputs.']}], 'duration': 1102.516, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY1615057.jpg', 'highlights': ['The activation functions introduce non-linearity, crucial for uncovering complex, hidden patterns in data.', 'The weights assigned to input features determine their contribution to hidden units, enabling the understanding of their impact on the hidden layer.', 'The structure includes a simple architecture with three hidden units, but it can be adapted to include more hidden layers.', 'The four popular activation functions mentioned are sigmoid, tanh, ReLU, and Leaky ReLU, each serving specific roles in transforming z-scores to activation values and handling saturation issues.']}, {'end': 4136.751, 'segs': [{'end': 2774.049, 'src': 'embed', 'start': 2744.492, 'weight': 0, 'content': [{'end': 2750.274, 'text': 'so this can be problematic for the cases when we do want to have the output.', 'start': 2744.492, 'duration': 5.782}, {'end': 2758.417, 'text': 'we do want to take into account these negative values and we want to consider these negative values and perform the predictions based on them too.', 'start': 2750.274, 'duration': 8.143}, {'end': 2764.659, 'text': 'in those cases we need to adjust this relu so we can then better use what we are calling leaky relu.', 'start': 2758.417, 'duration': 6.242}, {'end': 2774.049, 'text': 'And what this leaky rule activation function does is that it not only activates the positive scores, but it also activates the negative ones.', 'start': 2765.339, 'duration': 8.71}], 'summary': 'Using leaky relu allows considering and activating negative values for better predictions.', 'duration': 29.557, 'max_score': 2744.492, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY2744492.jpg'}, {'end': 2817.784, 'src': 'embed', 'start': 2790.964, 'weight': 1, 'content': [{'end': 2796.448, 'text': 'because the activation function corresponding to leaky relu can be represented by this f set,', 'start': 2790.964, 'duration': 5.484}, {'end': 2803.852, 'text': 'which is equal to 0.01 if z is more than zero and is equal to z if z is larger than equals zero.', 'start': 2796.448, 'duration': 7.404}, {'end': 2808.195, 'text': 'so for all the positive numbers, the leaky relu acts exactly the same.', 'start': 2803.852, 'duration': 4.343}, {'end': 2817.784, 'text': 'as for relu, as you can see here, but for the negative values, the corresponding activation is simply equal to 0.01.', 'start': 2808.195, 'duration': 9.589}], 'summary': 'Leaky relu has a 0.01 threshold for negative values, similar to relu for positive numbers.', 'duration': 26.82, 'max_score': 2790.964, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY2790964.jpg'}, {'end': 2888.135, 'src': 'embed', 'start': 2842.263, 'weight': 2, 'content': [{'end': 2846.325, 'text': 'And they are not so much used when it comes to output layer.', 'start': 2842.263, 'duration': 4.062}, {'end': 2854.289, 'text': 'So think of like using the leaky relu and relu for your hidden layers, but not to use them for your output layer.', 'start': 2846.765, 'duration': 7.524}, {'end': 2861.432, 'text': 'And the other way around for the sigmoid function and tank function, use them for your output layer, but not for your hidden layers.', 'start': 2854.669, 'duration': 6.763}, {'end': 2868.278, 'text': 'So the next question is what happens if you do not use any activation functions in a neural network?', 'start': 2861.992, 'duration': 6.286}, {'end': 2873.882, 'text': 'So the answer for this question can be very short, because it is quite obvious.', 'start': 2869.098, 'duration': 4.784}, {'end': 2883.491, 'text': 'So the absence of activation functions will reduce the neural network to a common machine learning model, like linear regression,', 'start': 2874.703, 'duration': 8.788}, {'end': 2888.135, 'text': 'something that removes the entire idea of using neural networks in the first place.', 'start': 2883.491, 'duration': 4.644}], 'summary': 'Using activation functions in neural network layers is crucial for its functionality and distinguishes it from linear regression models.', 'duration': 45.872, 'max_score': 2842.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY2842263.jpg'}, {'end': 2954.743, 'src': 'embed', 'start': 2930.715, 'weight': 4, 'content': [{'end': 2942.32, 'text': 'So you can start by describing the training process of neural network, a very basic one, by the process of what we are calling forward pass forward.', 'start': 2930.715, 'duration': 11.605}, {'end': 2954.743, 'text': 'pass takes the input data and processes through neurons which we saw before, and uses this weighted sum and activation functions to produce an output.', 'start': 2942.32, 'duration': 12.423}], 'summary': 'Describing the training process of a neural network, using forward pass to process input data through neurons and produce an output.', 'duration': 24.028, 'max_score': 2930.715, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY2930715.jpg'}, {'end': 3590.122, 'src': 'embed', 'start': 3561.46, 'weight': 6, 'content': [{'end': 3570.947, 'text': 'so the way that the gd is performing the optimization and updating the model parameters is taking the output of the back prop,', 'start': 3561.46, 'duration': 9.487}, {'end': 3577.252, 'text': 'which is the first order partial derivative of the loss function with respect to the model parameters,', 'start': 3570.947, 'duration': 6.305}, {'end': 3581.035, 'text': 'and then multiplying it by the learning rate or the step size,', 'start': 3577.252, 'duration': 3.783}, {'end': 3590.122, 'text': 'and then subtracting this amount from the original and current model parameters in order to get the updated version of the model parameters.', 'start': 3581.035, 'duration': 9.087}], 'summary': 'Gradient descent updates model parameters using backprop derivatives and learning rate.', 'duration': 28.662, 'max_score': 3561.46, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY3561460.jpg'}, {'end': 3708.228, 'src': 'embed', 'start': 3678.531, 'weight': 7, 'content': [{'end': 3684.696, 'text': 'If we take this learning rate very large, it means that we will apply a bigger change,', 'start': 3678.531, 'duration': 6.165}, {'end': 3692.66, 'text': 'which means the algorithm will make a bigger step when it comes to moving towards the global optimum.', 'start': 3684.696, 'duration': 7.964}, {'end': 3701.625, 'text': 'And later on, we will also see that it might become problematic when we are making too big of a jump, especially if those are not accurate jumps.', 'start': 3692.68, 'duration': 8.945}, {'end': 3708.228, 'text': 'So we need to therefore ensure that we optimize this learning parameter, which is a hyper parameter,', 'start': 3701.965, 'duration': 6.263}], 'summary': 'Optimizing learning rate is crucial for algorithm performance.', 'duration': 29.697, 'max_score': 3678.531, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY3678531.jpg'}, {'end': 3793.699, 'src': 'embed', 'start': 3770.099, 'weight': 8, 'content': [{'end': 3778.589, 'text': 'So the problem of the gradient descent is that when it is using the entire training data for every time updating the model parameters,', 'start': 3770.099, 'duration': 8.49}, {'end': 3783.154, 'text': 'it is just sometimes computationally not feasible or super expensive.', 'start': 3778.589, 'duration': 4.565}, {'end': 3793.699, 'text': 'Because training a lot of observations, taking the entire training data to perform just one update in your model parameters and every time,', 'start': 3783.695, 'duration': 10.004}], 'summary': 'Gradient descent can be computationally expensive when using entire training data for every update.', 'duration': 23.6, 'max_score': 3770.099, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY3770099.jpg'}, {'end': 3903.409, 'src': 'embed', 'start': 3877.318, 'weight': 5, 'content': [{'end': 3887.661, 'text': 'Gradient descent that we just discussed is one of such algorithms that is used in order to update the model parameters in order to minimize the amount of error that the model is making.', 'start': 3877.318, 'duration': 10.343}, {'end': 3894.345, 'text': 'In this case, the amount of cost or the loss that the model is making by minimizing the loss function.', 'start': 3887.782, 'duration': 6.563}, {'end': 3903.409, 'text': 'So that is basically the primary goal of the optimizers in not only deep learning but also in general in machine learning and in deep learning models.', 'start': 3895.365, 'duration': 8.044}], 'summary': 'Gradient descent minimizes model error in machine learning.', 'duration': 26.091, 'max_score': 3877.318, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY3877318.jpg'}, {'end': 4006.997, 'src': 'embed', 'start': 3957.023, 'weight': 10, 'content': [{'end': 3966.428, 'text': 'but there are also many other variants of gd that have been developed over the years in order to combat some of the disadvantages that the gd has,', 'start': 3957.023, 'duration': 9.405}, {'end': 3972.731, 'text': 'but, at the same time, also to try to replicate the benefits of the gradient descent.', 'start': 3966.428, 'duration': 6.303}, {'end': 3978.556, 'text': 'So the SGD is one example of optimization algorithm beside of GD.', 'start': 3973.032, 'duration': 5.524}, {'end': 3981.438, 'text': 'There is this mini batch GD.', 'start': 3979.217, 'duration': 2.221}, {'end': 3989.205, 'text': 'We also have SGD with momentum when the momentum was introduced to improve the optimization algorithm such as SGD.', 'start': 3982.199, 'duration': 7.006}, {'end': 3995.65, 'text': 'We also have adaptive learning based type of optimization techniques such as RMS prop.', 'start': 3989.845, 'duration': 5.805}, {'end': 3999.572, 'text': 'Adam and Adam W, also Ada Grad.', 'start': 3996.69, 'duration': 2.882}, {'end': 4006.997, 'text': 'And those are all different sorts of optimization algorithms that are used in deep learning in order to optimize the algorithm.', 'start': 3999.832, 'duration': 7.165}], 'summary': 'Various optimization algorithms like sgd, mini batch gd, rms prop, and ada grad are used in deep learning for optimization.', 'duration': 49.974, 'max_score': 3957.023, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY3957023.jpg'}, {'end': 4102.319, 'src': 'embed', 'start': 4053.311, 'weight': 9, 'content': [{'end': 4068.655, 'text': 'What this means is that when we have this area consisting of many values that the error can take In some cases when the optimization is making those movements in order to reach that minimum,', 'start': 4053.311, 'duration': 15.344}, {'end': 4077.204, 'text': 'it might confuse and end up discovering the local minimum or local maximum instead of finding the global minimum on global maximum.', 'start': 4068.655, 'duration': 8.549}, {'end': 4080.646, 'text': 'What this means is that, for some area,', 'start': 4077.825, 'duration': 2.821}, {'end': 4091.19, 'text': 'when the optimization algorithm is moving in order to understand how it should improve its direction to identify that minimum in some cases it might discover that,', 'start': 4080.646, 'duration': 10.544}, {'end': 4093.411, 'text': 'well, this is the minimum that we are looking.', 'start': 4091.19, 'duration': 2.221}, {'end': 4095.332, 'text': 'So the algorithm will converge.', 'start': 4093.511, 'duration': 1.821}, {'end': 4102.319, 'text': 'and it will decide that this is the set of hyperparameters and parameters that we need to use in order to optimize our model.', 'start': 4095.792, 'duration': 6.527}], 'summary': 'Optimization algorithms may converge to local instead of global minima/maxima, affecting model parameters.', 'duration': 49.008, 'max_score': 4053.311, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY4053311.jpg'}], 'start': 2717.573, 'title': 'Activation functions and optimization in neural networks', 'summary': 'Covers the limitations of relu and introduces leaky relu, the importance of activation functions, neural network training processes, gradient descent, learning rate significance, and optimization algorithms including gradient descent, stochastic gradient descent, rms prop, adam, and ada grad.', 'chapters': [{'end': 2861.432, 'start': 2717.573, 'title': 'Leaky relu activation function', 'summary': 'Discusses the limitations of relu activation function for negative values and introduces the concept of leaky relu as a solution, explaining how it activates negative values at a lesser extreme than positive ones, with a recommended usage for hidden layers but not for the output layer.', 'duration': 143.859, 'highlights': ['The leaky ReLU activation function not only activates positive scores but also activates negative ones at a lesser extreme, with a corresponding activation of 0.01 for negative values, making it a recommended choice for hidden layers (quantifiable: explanation of function and its effect on negative values).', 'The ReLU activation function sets the activation equal to zero for negative z-scores, which can be problematic when considering negative values for predictions (quantifiable: limitation of ReLU for negative values and need for adjustment).', 'The leaky ReLU and ReLU activation functions do not suffer from saturation unlike sigmoid and tanh functions, making them better options for hidden layer activations (quantifiable: comparison with other activation functions).']}, {'end': 3459.825, 'start': 2861.992, 'title': 'Activation functions and neural network training', 'summary': 'Discusses the importance of activation functions in neural networks to enable the discovery of hidden patterns, the training process involving forward pass, back propagation, and backward pass, and the role of gradient descent in optimizing model parameters to minimize loss functions.', 'duration': 597.833, 'highlights': ['The absence of activation functions reduces the neural network to a common machine learning model like linear regression, removing the idea of using neural networks (importance of activation functions).', 'The training process involves forward pass, which processes input data through neurons, applies weighted sum and activation functions to produce output, and back propagation computes the gradient of the loss function with respect to the weights and bias vector (training process of neural networks).', 'Gradient descent is an optimization algorithm used to minimize the loss function of the model and iteratively improve model parameters to produce highly accurate predictions (role of gradient descent).']}, {'end': 3836.36, 'start': 3460.345, 'title': 'Gradient descent in neural networks', 'summary': 'Explains the concept of gradient descent, its role in updating model parameters using backpropagation, the significance of learning rate in optimization, and the trade-off between computational efficiency and accuracy in deep learning.', 'duration': 376.015, 'highlights': ['The role of gradient descent in updating model parameters using backpropagation and the computation of first-order partial derivatives of the loss function with respect to each model parameter.', 'The significance of the learning rate in determining the size of steps taken during parameter updates and the need to optimize it to minimize the loss function and optimize the neural network.', 'The computational efficiency and accuracy trade-off in using gradient descent, where it is known as a good optimizer but may become computationally infeasible or expensive when dealing with very large or complex data.']}, {'end': 4136.751, 'start': 3836.36, 'title': 'Optimization algorithms in deep learning', 'summary': 'Discusses the goal of optimization algorithms in deep learning, including the iterative improvement of model parameters to minimize error and the utilization of various optimization algorithms such as gradient descent, stochastic gradient descent, rms prop, adam, and ada grad to achieve global optimum.', 'duration': 300.391, 'highlights': ['The goal of optimization algorithms is to iteratively improve model parameters to find the global optimum, ensuring the algorithm makes the minimum amount of error and accurately predicts unseen data.', 'Various optimization algorithms such as Stochastic Gradient Descent, RMS prop, Adam, and Ada Grad are used to update model parameters and minimize the loss function, ensuring accurate predictions and performance across different applications.', 'Different optimization algorithms like Stochastic Gradient Descent, RMS prop, Adam, and Ada Grad are employed to combat the disadvantages of Gradient Descent and maximize the objective function, enabling efficient model optimization in deep learning.', "Explanation of the concept of global optimum, distinguishing it from local minimum or maximum, and the potential confusion that optimization algorithms may encounter in differentiating between them, affecting the accuracy of the algorithm's convergence and finding the actual minimum or maximum."]}], 'duration': 1419.178, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY2717573.jpg', 'highlights': ['The leaky ReLU activation function activates negative values at a lesser extreme, recommended for hidden layers.', 'The ReLU activation function sets negative z-scores to zero, limiting predictions for negative values.', 'Leaky ReLU and ReLU do not suffer from saturation, making them better options for hidden layers.', 'The absence of activation functions reduces neural networks to common machine learning models.', 'The training process involves forward pass, weighted sum, activation functions, and backpropagation.', 'Gradient descent minimizes the loss function and iteratively improves model parameters.', 'Gradient descent updates model parameters using backpropagation and first-order partial derivatives.', 'The learning rate determines the size of steps during parameter updates and needs optimization.', 'Gradient descent is known as a good optimizer but may become computationally infeasible.', 'Optimization algorithms aim to iteratively improve model parameters and find the global optimum.', 'Various optimization algorithms like Stochastic Gradient Descent, RMS prop, Adam, and Ada Grad are used.', 'Different optimization algorithms combat the disadvantages of Gradient Descent and maximize the objective function.', 'Optimization algorithms may encounter confusion in differentiating between global and local optima.']}, {'end': 5153.823, 'segs': [{'end': 4752.702, 'src': 'embed', 'start': 4728.662, 'weight': 1, 'content': [{'end': 4740.752, 'text': 'But I would just say that the backpropagation is the actual process of computing the gradients to understand how much change in the loss function is there when we are changing the model parameters.', 'start': 4728.662, 'duration': 12.09}, {'end': 4752.702, 'text': 'And then the output of the backpropagation is simply used as an input for the gradient descent or any other optimization algorithm in order to update the model parameters.', 'start': 4741.512, 'duration': 11.19}], 'summary': 'Backpropagation computes gradients for updating model parameters.', 'duration': 24.04, 'max_score': 4728.662, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY4728662.jpg'}, {'end': 4841.396, 'src': 'embed', 'start': 4818.039, 'weight': 2, 'content': [{'end': 4830.468, 'text': 'and the result of this vanishing gradient is that the network is no longer able to learn dependencies in the data effectively and the model is no longer able to update the model effectively,', 'start': 4818.039, 'duration': 12.429}, {'end': 4841.396, 'text': 'which means that the algorithm will end up not being optimized and we will end up with a model that is unable and was not able to learn the actual dependencies in the data.', 'start': 4830.468, 'duration': 10.928}], 'summary': 'Vanishing gradient leads to ineffective learning and optimization in the network.', 'duration': 23.357, 'max_score': 4818.039, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY4818039.jpg'}, {'end': 5040.024, 'src': 'embed', 'start': 5012.475, 'weight': 0, 'content': [{'end': 5015.596, 'text': 'especially from the earlier layers when the gradient is vanishing.', 'start': 5012.475, 'duration': 3.121}, {'end': 5017.837, 'text': 'And this is then the problem,', 'start': 5016.476, 'duration': 1.361}, {'end': 5029.16, 'text': 'because we want our model to continuously learn and continuously update those weights such that we will end up with the best set of parameters for the weights and the bias factors,', 'start': 5017.837, 'duration': 11.323}, {'end': 5034.061, 'text': 'in order to minimize the loss function and then provide highly accurate predictions.', 'start': 5029.16, 'duration': 4.901}, {'end': 5040.024, 'text': 'Therefore, ideally, we want to ensure that those gradients do not vanish,', 'start': 5035.04, 'duration': 4.984}], 'summary': 'We want to prevent vanishing gradients to continuously update model weights for accurate predictions.', 'duration': 27.549, 'max_score': 5012.475, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5012475.jpg'}, {'end': 5153.823, 'src': 'embed', 'start': 5126.536, 'weight': 3, 'content': [{'end': 5135.89, 'text': 'So they are not sequential based like RNNs, then they are less likely to be prone to this vanishing gradient problem than the RNNs.', 'start': 5126.536, 'duration': 9.354}, {'end': 5139.633, 'text': 'Because RNNs have another sequence type of neural networks.', 'start': 5136.21, 'duration': 3.423}, {'end': 5149.52, 'text': 'they are inherently deep, which means that they have multiple layers based on the amount of time steps that they have,', 'start': 5139.633, 'duration': 9.887}, {'end': 5153.823, 'text': 'which then makes these algorithms more prone to the vanishing gradient problem.', 'start': 5149.52, 'duration': 4.303}], 'summary': 'Cnns are less prone to vanishing gradient problem than rnns due to their non-sequential nature.', 'duration': 27.287, 'max_score': 5126.536, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5126536.jpg'}], 'start': 4137.612, 'title': 'Back propagation and vanishing gradient problem', 'summary': 'Delves into the significance of back propagation in deep learning, highlighting its role in training neural networks and optimizing model parameters. it also covers the vanishing gradient problem in rnns and lstms, discussing its impact on effective learning.', 'chapters': [{'end': 4547.318, 'start': 4137.612, 'title': 'Back propagation in deep learning', 'summary': 'Discusses the importance of back propagation in deep learning, outlining its role in training neural networks by iteratively adjusting the weights and bias factors using gradients to minimize the loss function and optimize model parameters for better prediction accuracy.', 'duration': 409.706, 'highlights': ['The back propagation algorithm is essential for training neural networks, as it iteratively adjusts the weights and bias factors based on the gradients of the loss function, aiming to minimize errors and improve prediction accuracy.', 'Back propagation involves computing gradients of the loss function with respect to model parameters, such as weight and bias factors, to understand the impact of parameter changes on the loss function and enable optimization algorithms like SGD and GADM to update the model parameters effectively.', 'The process of back propagation includes calculating the first-order partial derivatives of the loss function with respect to each model parameter, enabling the algorithm to understand the impact of parameter changes on the loss function and facilitate efficient model parameter updates.', 'To update model parameters, such as weights and bias factors, the gradients of the loss function with respect to the parameters, and the gradients of the activations and z-scores, need to be computed using the chain rule and differentiation rules, enabling the algorithm to iteratively adjust the parameters for better prediction accuracy.']}, {'end': 4891.348, 'start': 4548.228, 'title': 'Backpropagation and gradient descent', 'summary': 'Explains the process of backpropagation, its role in optimizing algorithms, and the difference between backpropagation and gradient descent, emphasizing the vanishing gradient problem and its impact on neural networks.', 'duration': 343.12, 'highlights': ['The backpropagation process involves computing gradients of the loss function with respect to the model parameters to be used as input for optimization algorithms like gradient descent, enabling iterative updates of model parameters from deeper layers to earlier layers.', "Vanishing gradients occur when the gradients become very small or entirely close to zero as they propagate back through deep layers, leading to the network's inability to effectively learn data dependencies and update the model parameters, ultimately resulting in an unoptimized model with inaccurate predictions.", 'The difference between backpropagation and gradient descent lies in the fact that backpropagation computes the gradients for model parameter changes, and the output is used as input for gradient descent or other optimization algorithms to update the model parameters, while gradient descent uses the computed gradients from backpropagation to iteratively update the model parameters.']}, {'end': 5153.823, 'start': 4891.348, 'title': 'Vanishing gradient problem in neural networks', 'summary': "Discusses the vanishing gradient problem in neural networks, particularly in rnns and lstms, which arise due to the multiplication of weight values leading to gradients becoming close to zero, hindering the network's ability to learn effectively.", 'duration': 262.475, 'highlights': ["The vanishing gradient problem occurs in deep neural networks, especially in RNNs and LSTMs, due to the multiplication of weight values leading to gradients becoming close to zero, hindering the network's ability to learn effectively.", 'RNNs and LSTMs, being inherently deep in nature, are more prone to the vanishing gradient problem due to their sequential architecture and multiple layers based on time steps, making them susceptible to gradients becoming close to zero.']}], 'duration': 1016.211, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY4137612.jpg', 'highlights': ['Back propagation iteratively adjusts weights and bias factors to minimize errors and improve prediction accuracy.', 'Back propagation computes gradients of the loss function to understand the impact of parameter changes.', "Vanishing gradients hinder the network's ability to effectively learn data dependencies and update model parameters.", 'RNNs and LSTMs are more prone to the vanishing gradient problem due to their sequential architecture and multiple layers.']}, {'end': 6336.464, 'segs': [{'end': 5180.737, 'src': 'embed', 'start': 5154.537, 'weight': 5, 'content': [{'end': 5160.221, 'text': 'The next question is what is the connection between various activation functions and the vanishing gradient problem?', 'start': 5154.537, 'duration': 5.684}, {'end': 5167.126, 'text': 'So, vanishing gradient problem is highly related to the choice of activation functions,', 'start': 5161.102, 'duration': 6.024}, {'end': 5175.673, 'text': 'because there are certain activation functions that automatically result in vanishing gradient problem because of their inherent characteristics.', 'start': 5167.126, 'duration': 8.547}, {'end': 5180.737, 'text': 'And there are also activation functions which are known to not cause the vanishing gradient problem.', 'start': 5176.213, 'duration': 4.524}], 'summary': "Activation functions impact vanishing gradient; some cause problem, others don't.", 'duration': 26.2, 'max_score': 5154.537, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5154537.jpg'}, {'end': 5464.676, 'src': 'embed', 'start': 5438.279, 'weight': 1, 'content': [{'end': 5447.325, 'text': 'So one thing that we can keep in mind is that the sigmoid activation function and Tang activation functions are great when it comes to the output layer,', 'start': 5438.279, 'duration': 9.046}, {'end': 5452.549, 'text': 'because they have these nice properties in transforming the values between certain ranges.', 'start': 5447.325, 'duration': 5.224}, {'end': 5457.992, 'text': 'Like in case of sigmoid function, it transforms the values, any values between 0 and 1.', 'start': 5452.969, 'duration': 5.023}, {'end': 5464.676, 'text': 'which is great when we want to have an output in the form of probabilities, great for classification type of problems.', 'start': 5457.992, 'duration': 6.684}], 'summary': 'Sigmoid and tang activation functions are ideal for output layers, transforming values between specific ranges for classification problems.', 'duration': 26.397, 'max_score': 5438.279, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5438279.jpg'}, {'end': 5508.498, 'src': 'embed', 'start': 5479.925, 'weight': 0, 'content': [{'end': 5485.508, 'text': 'but then do not use it for your hidden layers, because they are prone to vanishing gradient problem.', 'start': 5479.925, 'duration': 5.583}, {'end': 5494.606, 'text': 'The next set of activation functions that we will discuss today are the rectifier linear unit and the leaky rectifier linear unit.', 'start': 5486.38, 'duration': 8.226}, {'end': 5503.555, 'text': 'Unlike the sigmoid activation function and the tang activation function, ReLU or leaky ReLU, they both do not suffer from saturation.', 'start': 5495.306, 'duration': 8.249}, {'end': 5508.498, 'text': 'This means that they are then no longer causing the vanishing gradient problem.', 'start': 5504.096, 'duration': 4.402}], 'summary': 'Relu and leaky relu avoid vanishing gradient problem.', 'duration': 28.573, 'max_score': 5479.925, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5479925.jpg'}, {'end': 5720.161, 'src': 'embed', 'start': 5686.442, 'weight': 7, 'content': [{'end': 5692.545, 'text': 'which means that we are not using the right distribution to randomly sample from it,', 'start': 5686.442, 'duration': 6.103}, {'end': 5704.409, 'text': 'or we are just providing a wrong or not proper value to this weight and the bias parameters, it means that this will, right from the beginning,', 'start': 5692.545, 'duration': 11.864}, {'end': 5708.111, 'text': 'skew off the learning process for those specific neurons.', 'start': 5704.409, 'duration': 3.702}, {'end': 5720.161, 'text': 'And if we right from the beginning perform the learning process in an improper way and we are skewing those learning processes in every iteration,', 'start': 5709.031, 'duration': 11.13}], 'summary': 'Improper distribution or values skew learning process for neurons.', 'duration': 33.719, 'max_score': 5686.442, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5686442.jpg'}, {'end': 5787.687, 'src': 'embed', 'start': 5764.295, 'weight': 6, 'content': [{'end': 5781.823, 'text': 'because we are no longer able to effectively learn those dependencies in the model and that specific neuron will not be correctly activated and the corresponding weights will not be updated in order to improve the accuracy of the predictions for those specific neurons.', 'start': 5764.295, 'duration': 17.528}, {'end': 5787.687, 'text': 'and of course, the same holds for the opposite problem, which is the exploding gradient problem.', 'start': 5782.523, 'duration': 5.164}], 'summary': 'Ineffective learning of dependencies leads to incorrect neuron activation and weight update in the model, affecting prediction accuracy.', 'duration': 23.392, 'max_score': 5764.295, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5764295.jpg'}, {'end': 5832.078, 'src': 'embed', 'start': 5811.783, 'weight': 2, 'content': [{'end': 5822.431, 'text': 'we might be overshooting the optimal values during the training or undershooting so insufficiently updating those weight parameters and those bias parameters,', 'start': 5811.783, 'duration': 10.648}, {'end': 5828.195, 'text': 'and this means that we will then continuously make large errors for those specific neurons.', 'start': 5822.431, 'duration': 5.764}, {'end': 5832.078, 'text': "therefore, it's really important to optimize these hyper parameters in our model,", 'start': 5828.195, 'duration': 3.883}], 'summary': 'Optimizing hyperparameters is crucial for avoiding large errors in specific neurons during training.', 'duration': 20.295, 'max_score': 5811.783, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5811783.jpg'}, {'end': 5898.472, 'src': 'embed', 'start': 5872.199, 'weight': 3, 'content': [{'end': 5880.903, 'text': 'So, computational graph is a way to visualize complex operations that we are using as part of training models,', 'start': 5872.199, 'duration': 8.704}, {'end': 5884.545, 'text': 'as part of various type of optimization processes.', 'start': 5880.903, 'duration': 3.642}, {'end': 5898.472, 'text': "And it's a great way to showcase all the steps that we are taking when going from very simple variables or objects to applying various transformations and then getting to the very complex functions.", 'start': 5885.165, 'duration': 13.307}], 'summary': 'Computational graph visualizes complex operations in training models and optimization processes.', 'duration': 26.273, 'max_score': 5872.199, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5872199.jpg'}, {'end': 6315.07, 'src': 'embed', 'start': 6289.469, 'weight': 4, 'content': [{'end': 6299.921, 'text': 'because exploring gradients means that we have unstable neural network, and this also means that the update of the weights will be too large,', 'start': 6289.469, 'duration': 10.452}, {'end': 6305.652, 'text': 'and this will then result in not well performing and not properly trained neural network.', 'start': 6299.921, 'duration': 5.731}, {'end': 6315.07, 'text': 'Now, to avoid all these problems, the way we can solve this exploding gradient problem is by using the gradient clipping.', 'start': 6306.644, 'duration': 8.426}], 'summary': 'Exploding gradients can be avoided using gradient clipping to prevent unstable neural network and improve performance.', 'duration': 25.601, 'max_score': 6289.469, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY6289469.jpg'}], 'start': 5154.537, 'title': 'Activation functions, vanishing gradient, and error analysis', 'summary': 'Discusses the impact of activation functions on the vanishing gradient problem, recommends relu and leaky relu to prevent saturation, and explores error analysis in neural networks including causes of large errors, computational graph visualization, and the importance of gradient clipping.', 'chapters': [{'end': 5190.603, 'start': 5154.537, 'title': 'Activation functions and vanishing gradient', 'summary': 'Discusses the link between activation functions and the vanishing gradient problem, highlighting how certain functions can lead to this issue, while others do not, and the importance of being aware of these distinctions.', 'duration': 36.066, 'highlights': ['The vanishing gradient problem is closely connected to the choice of activation functions.', 'Certain activation functions inherently lead to the vanishing gradient problem.', 'Some activation functions are known to not cause the vanishing gradient problem.']}, {'end': 5636.467, 'start': 5191.484, 'title': 'Activation functions and vanishing gradient', 'summary': 'Explains the concept of saturation in sigmoid and tanh activation functions, causing vanishing gradient problem, and recommends using relu and leaky relu for hidden layers due to their non-saturation property and effectiveness in preventing vanishing gradients.', 'duration': 444.983, 'highlights': ['The ReLU and Leaky ReLU activation functions do not suffer from saturation, preventing vanishing gradient problem and making them ideal for hidden layers.', 'The sigmoid and tanh activation functions suffer from saturation, causing vanishing gradient problem, and are recommended for output layers but not for hidden layers.', 'The sigmoid and tanh activation functions transform values to specific ranges, making them suitable for output layers, especially for classification problems.']}, {'end': 6336.464, 'start': 5637.348, 'title': 'Neural network error analysis', 'summary': 'Discusses the reasons for large errors in specific neurons during backpropagation in neural networks, emphasizing poor weight initialization, vanishing or exploding gradients, inadequate learning rate, and improper activation functions. it also explains the concept of computational graph and its role in visualizing complex operations in training models. additionally, it delves into the significance of gradient clipping in addressing the exploding gradient problem.', 'duration': 699.116, 'highlights': ['The importance of proper weight initialization, including the impact of using the right distribution and values, on skewing the learning process, resulting in consistently large errors for specific neurons.', 'The detrimental effects of vanishing or exploding gradients, especially in deep networks, on the ability to learn dependencies, activate neurons, and update corresponding weights, leading to persistent large errors for specific neurons.', 'The adverse impact of inadequate learning rate, either too high or too small, on overshooting or undershooting optimal values during training, resulting in continuously large errors for specific neurons.', 'The significance of optimizing hyperparameters, including activation functions, for proper learning of dependencies in data and accurate updating of weight parameters.', 'The role of computational graph in visualizing complex operations, including the representation of neural networks using nodes and edges to showcase the process of computing z-scores, applying activation functions, and computing predictions.', 'The explanation of gradient clipping as a solution to the exploding gradient problem, involving the clipping of gradients at a certain threshold to prevent overly large gradients and ensure stability in the neural network.']}], 'duration': 1181.927, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY5154537.jpg', 'highlights': ['The ReLU and Leaky ReLU activation functions prevent vanishing gradient problem.', 'The sigmoid and tanh activation functions are recommended for output layers.', 'The importance of optimizing hyperparameters for proper learning of dependencies.', 'The role of computational graph in visualizing complex operations.', 'The explanation of gradient clipping as a solution to the exploding gradient problem.', 'The vanishing gradient problem is closely connected to the choice of activation functions.', 'The detrimental effects of vanishing or exploding gradients, especially in deep networks.', 'The significance of proper weight initialization on skewing the learning process.', 'The adverse impact of inadequate learning rate on overshooting or undershooting optimal values.']}, {'end': 9211.433, 'segs': [{'end': 6365.777, 'src': 'embed', 'start': 6336.844, 'weight': 0, 'content': [{'end': 6342.232, 'text': 'and we are not updating our weight parameters or bias factors too much.', 'start': 6336.844, 'duration': 5.388}, {'end': 6351.808, 'text': 'So, in this way, what we are doing is that we are kind of stabilizing our neural network, which is especially important for architectures like LSTMs,', 'start': 6343.362, 'duration': 8.446}, {'end': 6357.531, 'text': 'RNNs, GRUs, which have this sequential nature of the data with too many layers.', 'start': 6351.808, 'duration': 5.723}, {'end': 6365.777, 'text': 'And in this way, we will ensure that we do not have these erratic jumps in our neural network optimization process.', 'start': 6358.372, 'duration': 7.405}], 'summary': 'Stabilizing neural network for sequential data with many layers, preventing erratic jumps.', 'duration': 28.933, 'max_score': 6336.844, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY6336844.jpg'}, {'end': 6537.171, 'src': 'embed', 'start': 6508.274, 'weight': 9, 'content': [{'end': 6512.736, 'text': 'or this image has 30% probability of being a dog image.', 'start': 6508.274, 'duration': 4.462}, {'end': 6520.619, 'text': 'In all those cases, when you are dealing with this type of problems, you can apply the cross entropy as a loss function.', 'start': 6513.476, 'duration': 7.143}, {'end': 6530.644, 'text': 'And the cross entropy is measured as this negative of the sum of the y log p plus 1 minus y and then log 1 minus p.', 'start': 6521.5, 'duration': 9.144}, {'end': 6533.531, 'text': 'where y is the actual label.', 'start': 6531.69, 'duration': 1.841}, {'end': 6537.171, 'text': 'So in binary classification, this can be, for instance, 1 and 0.', 'start': 6534.051, 'duration': 3.12}], 'summary': 'Cross entropy used for binary classification with 30% dog image probability', 'duration': 28.897, 'max_score': 6508.274, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY6508274.jpg'}, {'end': 6670.574, 'src': 'embed', 'start': 6638.154, 'weight': 1, 'content': [{'end': 6640.958, 'text': 'which is often referred as a softmax function.', 'start': 6638.154, 'duration': 2.804}, {'end': 6652.591, 'text': 'So Softmax loss function is a great way to measure the performance of a model that wants to classify observations to one of the multiple classes,', 'start': 6641.899, 'duration': 10.692}, {'end': 6659.197, 'text': 'which means that we are no longer dealing with binary classification, but we are dealing with multiclass classification.', 'start': 6652.591, 'duration': 6.606}, {'end': 6670.574, 'text': 'So one example of such case is when we want to classify an image to be from summer theme, to be from spring theme or from winter theme.', 'start': 6660.106, 'duration': 10.468}], 'summary': 'Softmax loss function measures multiclass model performance.', 'duration': 32.42, 'max_score': 6638.154, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY6638154.jpg'}, {'end': 7424.012, 'src': 'embed', 'start': 7397.969, 'weight': 7, 'content': [{'end': 7404.212, 'text': 'And this imperfection can result in updates that do not always point directly towards the global optimum.', 'start': 7397.969, 'duration': 6.243}, {'end': 7407.934, 'text': 'And this will then cause these oscillations in the SGD.', 'start': 7404.732, 'duration': 3.202}, {'end': 7415.659, 'text': 'So at high level, I would say that there are three reasons why SGT will have too many of these oscillations.', 'start': 7409.369, 'duration': 6.29}, {'end': 7417.862, 'text': 'The first one is the random subsets.', 'start': 7416.1, 'duration': 1.762}, {'end': 7419.545, 'text': 'The second one is the step size.', 'start': 7418.022, 'duration': 1.523}, {'end': 7424.012, 'text': 'And the third one is definitely the imperfect estimate of the gradients.', 'start': 7419.905, 'duration': 4.107}], 'summary': 'Sgd oscillations caused by random subsets, step size, and imperfect gradients.', 'duration': 26.043, 'max_score': 7397.969, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY7397969.jpg'}, {'end': 7807.293, 'src': 'embed', 'start': 7781.756, 'weight': 4, 'content': [{'end': 7793.604, 'text': 'So to solve this problem and to improve the SGD algorithm, while taking into account that SGD in many aspects is much better than the GD,', 'start': 7781.756, 'duration': 11.848}, {'end': 7796.526, 'text': 'we came up with this SGD with momentum algorithm,', 'start': 7793.604, 'duration': 2.922}, {'end': 7804.971, 'text': 'where SGD with momentum will take basically the benefits of the SGD and then it will also try to address the biggest disadvantage of SGD,', 'start': 7796.526, 'duration': 8.445}, {'end': 7807.293, 'text': 'which is too many of these oscillations.', 'start': 7804.971, 'duration': 2.322}], 'summary': "Proposed sgd with momentum to address sgd's oscillations.", 'duration': 25.537, 'max_score': 7781.756, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY7781756.jpg'}, {'end': 8077.857, 'src': 'embed', 'start': 8056.716, 'weight': 3, 'content': [{'end': 8068.821, 'text': 'which helps also to introduce consistency in the updates and to reduce the oscillations the algorithm is making by having much more smarter path towards discovering the actual global optimum of the loss function.', 'start': 8056.716, 'duration': 12.105}, {'end': 8077.857, 'text': 'The next question is, compare batch gradient descent to mini-batch gradient descent and to stochastic gradient descent.', 'start': 8070.435, 'duration': 7.422}], 'summary': 'Using smarter path reduces oscillations in updates.', 'duration': 21.141, 'max_score': 8056.716, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY8056716.jpg'}, {'end': 8211.74, 'src': 'embed', 'start': 8171.329, 'weight': 2, 'content': [{'end': 8174.731, 'text': 'It needs to every time put this entire training data into the memory.', 'start': 8171.329, 'duration': 3.402}, {'end': 8183.196, 'text': 'And it is very slow when it comes to performing the optimization, especially when dealing with large and complex data sets.', 'start': 8175.231, 'duration': 7.965}, {'end': 8188.46, 'text': 'Now, next, we have the other extreme of the batch gradient descent, which is SGD.', 'start': 8183.576, 'duration': 4.884}, {'end': 8198.209, 'text': 'So SGD, unlike the GD and we saw this previously when discussing the previous interview questions that SGD is using stochastically,', 'start': 8189.141, 'duration': 9.068}, {'end': 8205.895, 'text': 'so randomly sampled single or just few training observations in order to perform the training, so computing the gradients,', 'start': 8198.209, 'duration': 7.686}, {'end': 8211.74, 'text': 'performing the backprop and then using optimization to update the model parameters in each iteration.', 'start': 8205.895, 'duration': 5.845}], 'summary': 'Gd is slow with large data; sgd uses random samples for faster training.', 'duration': 40.411, 'max_score': 8171.329, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY8171329.jpg'}, {'end': 8671.011, 'src': 'embed', 'start': 8639.309, 'weight': 6, 'content': [{'end': 8645.553, 'text': "So just to summarize, when it comes to generalization, because it's really important, when we have small batch size,", 'start': 8639.309, 'duration': 6.244}, {'end': 8649.255, 'text': 'then we have a potentially better generalizing model,', 'start': 8645.553, 'duration': 3.702}, {'end': 8663.004, 'text': 'because we will then using a small portion of the training data and the model will be less inclined to memorize the training data and it will perform equally well on the new unseen data versus the large batch size,', 'start': 8649.255, 'duration': 13.749}, {'end': 8671.011, 'text': 'which will intend and will have higher likelihood of following the noise in the data and then not generalizing well on the unseen data.', 'start': 8663.004, 'duration': 8.007}], 'summary': 'Small batch size leads to better generalization, less memorization, and equal performance on new data.', 'duration': 31.702, 'max_score': 8639.309, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY8639309.jpg'}, {'end': 8733.286, 'src': 'embed', 'start': 8707.009, 'weight': 8, 'content': [{'end': 8712.83, 'text': 'which also means that the bias will most likely be higher in case of large batch size.', 'start': 8707.009, 'duration': 5.821}, {'end': 8717.714, 'text': 'So, when it comes to the variance, then in case of small batch size,', 'start': 8713.79, 'duration': 3.924}, {'end': 8724.259, 'text': 'the variance is higher because we are every time using a different part of the training data and small part.', 'start': 8717.714, 'duration': 6.545}, {'end': 8731.045, 'text': 'So there will be much more of these oscillations and the exploration when it comes to finding the global optimum.', 'start': 8724.679, 'duration': 6.366}, {'end': 8733.286, 'text': 'Whereas in case of large batch size,', 'start': 8731.525, 'duration': 1.761}], 'summary': 'Small batch size leads to higher variance, large batch size leads to higher bias.', 'duration': 26.277, 'max_score': 8707.009, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY8707009.jpg'}], 'start': 6336.844, 'title': 'Neural network stabilization and optimization techniques', 'summary': 'Explores stabilizing neural networks, use of cross entropy for classification, sgd in neural networks, addressing sgd oscillation issues, comparison of sgd with momentum and gradient descent, and the impact of batch size on deep learning, providing insights into key techniques and their impact on model performance.', 'chapters': [{'end': 6788.857, 'start': 6336.844, 'title': 'Stabilizing neural networks & cross entropy in classification', 'summary': 'Discusses stabilizing neural networks by not updating weight parameters or bias factors too much, and the use of cross entropy as a preferred cost function for multi-class classification, which measures the performance of a classification model with output probabilities. it also explores the application of multi-class cross entropy, also known as softmax function, for multi-class classification to measure the performance of a model that wants to classify observations to one of the multiple classes.', 'duration': 452.013, 'highlights': ['Stabilizing neural networks by not updating weight parameters or bias factors too much is important for architectures like LSTMs, RNNs, GRUs, which have a sequential nature of the data with too many layers, and ensures a stable network that properly learns interdependencies.', 'Cross entropy, also known as log loss, measures the performance of a classification model with output probabilities between zero and one, making it suitable for multi-class classification problems like classifying images of cats or dogs and old versus new houses.', 'The cross entropy is measured as the negative of the sum of the y log p plus 1 minus y and then log 1 minus p, where y is the actual label and p is the predicted probability, and is used for binary classification with values between 0 and 1.', 'The multi-class cross entropy, also known as softmax function, is used for multi-class classification and measures the performance of a model that wants to classify observations to one of the multiple classes, providing probabilities for each class per observation and ensuring well-separated classes.']}, {'end': 7084.744, 'start': 6788.857, 'title': 'Sgd in neural networks', 'summary': 'Explains the concept of stochastic gradient descent (sgd) in training neural networks, highlighting its use of randomly selected training observations for parameter updates, the trade-off between efficiency and accuracy, and its tendency to converge to local optima instead of global optima.', 'duration': 295.887, 'highlights': ['SGD is an optimization algorithm used in deep learning to optimize the performance of a model and find parameters that minimize the loss function, using randomly selected training observations, making the optimization process more efficient (e.g., faster updates) but less accurate due to noisy gradients.', 'Unlike Gradient Descent (GD) which uses the entire training data for each update, SGD uses just single or randomly selected few training observations for parameter updates, leading to quicker updates but imperfect estimates of true gradients.', 'The use of noisy gradients in SGD results in erratic movements during optimization, often leading to the discovery of local optima instead of the global optimum, making it less accurate and known to be a bad optimizer despite its efficiency in terms of convergence time and memory usage.']}, {'end': 7780.976, 'start': 7084.744, 'title': 'Sgd oscillation issues and improving optimization techniques', 'summary': 'Discusses the reasons for oscillation in stochastic gradient descent (sgd), including the impact of random subsets, step size, and imperfect estimates, and compares gradient descent (gd) and sgd in terms of data usage, update frequency, computational efficiency, and convergence pattern.', 'duration': 696.232, 'highlights': ['The impact of random subsets, step size, and imperfect estimates on the oscillation in SGD is discussed, with random subsets being the primary reason for excessive oscillation.', 'The differences between GD and SGD are outlined, including data usage, update frequency, computational efficiency, and convergence pattern, highlighting the computational efficiency and faster convergence of SGD.', 'The concept of SGD with momentum as an improved version of SGD is explained, emphasizing its role in reducing oscillations and producing more accurate updates.']}, {'end': 8540.301, 'start': 7781.756, 'title': 'Sgd with momentum & gradient descent comparison', 'summary': 'Introduces sgd with momentum as an improvement to the traditional sgd, explaining its concept, mathematical representation, and impact on optimization. it also compares batch gradient descent, mini-batch gradient descent, and stochastic gradient descent, highlighting their differences in efficiency, data usage, and algorithm quality.', 'duration': 758.545, 'highlights': ['The SGD with momentum algorithm introduces the concept of momentum to improve the SGD algorithm by reducing oscillations, using recent updates more heavily, and achieving more consistent updates, leading to improved optimization quality and discovering the global optimum rather than local optimum.', 'The mini-batch gradient descent strikes a balance between batch gradient descent and stochastic gradient descent by using randomly sampled training observations in batches, combining the efficiency of SGD with the stability and consistency of GD, resulting in improved optimization quality and consistency of updates.', 'The batch gradient descent uses the entire training data, providing high-quality and stable updates, but is inefficient and slow, especially with large and complex datasets, while SGD uses randomly sampled single or few training observations for quicker updates but sacrifices quality due to noisy gradients and oscillations.']}, {'end': 9211.433, 'start': 8540.301, 'title': 'Impact of batch size on deep learning', 'summary': 'Discusses the impact of batch size on deep learning models, highlighting that smaller batch sizes lead to potentially better generalization and lower bias, while larger batch sizes result in slower convergence, higher computational cost, and potential overfitting. furthermore, it explains the role of hessian in providing more accurate estimates of gradients in deep learning optimization, while also mentioning its computational challenges and disadvantages.', 'duration': 671.132, 'highlights': ['Smaller batch sizes lead to potentially better generalization and lower bias, while larger batch sizes result in slower convergence, higher computational cost, and potential overfitting.', 'Hessian provides more accurate estimates of gradients in deep learning optimization, resulting in smoother direction towards the global optimum, but comes with computational challenges and disadvantages.', 'Small batch sizes lead to potentially better generalization and lower bias, whereas large batch sizes generalize worse and likely have higher bias.', 'Using smaller batch sizes results in higher variance due to more fluctuations in exploring the training data, while larger batch sizes lead to lower variance with fewer explorations in finding the global optimum.', 'Smaller batch sizes require longer training time per epoch and more memory, impacting the convergence quality, learning dynamics, and ability to escape the local optimum.']}], 'duration': 2874.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY6336844.jpg', 'highlights': ['Stabilizing neural networks by not updating weight parameters or bias factors too much is important for architectures like LSTMs, RNNs, GRUs, ensuring a stable network that properly learns interdependencies.', 'The multi-class cross entropy, also known as softmax function, is used for multi-class classification and measures the performance of a model that wants to classify observations to one of the multiple classes.', 'The batch gradient descent uses the entire training data, providing high-quality and stable updates, but is inefficient and slow, especially with large and complex datasets.', 'The mini-batch gradient descent strikes a balance between batch gradient descent and stochastic gradient descent by using randomly sampled training observations in batches, resulting in improved optimization quality and consistency of updates.', 'The concept of SGD with momentum as an improved version of SGD is explained, emphasizing its role in reducing oscillations and producing more accurate updates.', 'SGD is an optimization algorithm used in deep learning to optimize the performance of a model and find parameters that minimize the loss function, using randomly selected training observations, making the optimization process more efficient but less accurate due to noisy gradients.', 'Smaller batch sizes lead to potentially better generalization and lower bias, while larger batch sizes result in slower convergence, higher computational cost, and potential overfitting.', 'The impact of random subsets, step size, and imperfect estimates on the oscillation in SGD is discussed, with random subsets being the primary reason for excessive oscillation.', 'Using smaller batch sizes results in higher variance due to more fluctuations in exploring the training data, while larger batch sizes lead to lower variance with fewer explorations in finding the global optimum.', 'Cross entropy, also known as log loss, measures the performance of a classification model with output probabilities between zero and one, making it suitable for multi-class classification problems like classifying images of cats or dogs and old versus new houses.']}, {'end': 10449.99, 'segs': [{'end': 9258.537, 'src': 'embed', 'start': 9211.753, 'weight': 6, 'content': [{'end': 9220.919, 'text': 'Then the other disadvantage of this Haitian usage is that we might have a risk of overfitting because when we are using this second order derivative,', 'start': 9211.753, 'duration': 9.166}, {'end': 9230.785, 'text': 'the second order partial derivative, as a way to estimate the two gradients, we might be over relying on this Haitian for training acceleration.', 'start': 9220.919, 'duration': 9.866}, {'end': 9240.87, 'text': 'And this might result in the model memorizing the training data and overly relying on the training data when performing the parameter updates,', 'start': 9231.625, 'duration': 9.245}, {'end': 9248.753, 'text': 'which will mean that the model will less likely generalize well on non-seen data,', 'start': 9240.87, 'duration': 7.883}, {'end': 9258.537, 'text': 'which is of course a problem because then our model is overfitting and it will be able to generalize well on an unseen data, which is our end goal.', 'start': 9248.753, 'duration': 9.784}], 'summary': 'Using second order derivative may lead to overfitting, impacting model generalization on unseen data.', 'duration': 46.784, 'max_score': 9211.753, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY9211753.jpg'}, {'end': 9337.225, 'src': 'embed', 'start': 9308.391, 'weight': 5, 'content': [{'end': 9317.537, 'text': 'Now, the adaptive learning process and adaptive learning rate in a neural network is quite different from this constant learning rate.', 'start': 9308.391, 'duration': 9.146}, {'end': 9329.343, 'text': 'So this learning rate, which we saw before defined by eta in deep learning, can play a crucial role when it comes to defining the step size.', 'start': 9318.058, 'duration': 11.285}, {'end': 9337.225, 'text': 'so how much we need to update the weight parameters, how much we need to update the bias factors?', 'start': 9329.343, 'duration': 7.882}], 'summary': 'Adaptive learning rate in neural network impacts weight and bias updates.', 'duration': 28.834, 'max_score': 9308.391, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY9308391.jpg'}, {'end': 9426.648, 'src': 'embed', 'start': 9402.192, 'weight': 3, 'content': [{'end': 9407.834, 'text': 'So from the learning process, from computing the gradients, and then from these patterns in the different features.', 'start': 9402.192, 'duration': 5.642}, {'end': 9419.102, 'text': 'So in this way, we are adopting the learning rate accordingly, and each model parameter will have its own learning rate.', 'start': 9408.214, 'duration': 10.888}, {'end': 9426.648, 'text': 'So this will then avoid having stagnation and will definitely reduce the oscillations,', 'start': 9420.083, 'duration': 6.565}], 'summary': 'Adapting learning rates individually reduces stagnation and oscillations.', 'duration': 24.456, 'max_score': 9402.192, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY9402192.jpg'}, {'end': 9478.457, 'src': 'embed', 'start': 9449.164, 'weight': 8, 'content': [{'end': 9450.645, 'text': 'there are a few that you can mention.', 'start': 9449.164, 'duration': 1.481}, {'end': 9458.891, 'text': 'And one of the most popular optimization algorithms that is also of adaptive nature is the ADAM optimization algorithm.', 'start': 9450.985, 'duration': 7.906}, {'end': 9465.016, 'text': 'So ADAM specifically stands for adaptive moment estimation,', 'start': 9459.731, 'duration': 5.285}, {'end': 9474.666, 'text': 'which is then the algorithm that adapts the learning rate accordingly and therefore is also known to have much better performance when it comes to optimization.', 'start': 9465.016, 'duration': 9.65}, {'end': 9478.457, 'text': 'So another example is the RMSPROP.', 'start': 9475.754, 'duration': 2.703}], 'summary': 'Adam and rmsprop are popular adaptive optimization algorithms with better performance.', 'duration': 29.293, 'max_score': 9449.164, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY9449164.jpg'}, {'end': 9627.775, 'src': 'embed', 'start': 9599.173, 'weight': 1, 'content': [{'end': 9608.915, 'text': 'Or when the gradients are so big that they are exploding, so they are becoming very large and they result in a large amount of oscillations.', 'start': 9599.173, 'duration': 9.742}, {'end': 9611.117, 'text': 'now to avoid this.', 'start': 9609.335, 'duration': 1.782}, {'end': 9621.028, 'text': "what rms prop is doing is that it is using an adaptive learning rate, it's adjusting the learning rate and it is using for this, for this process,", 'start': 9611.117, 'duration': 9.911}, {'end': 9627.775, 'text': 'this idea of running a running average of the second order gradients.', 'start': 9621.028, 'duration': 6.747}], 'summary': 'Rmsprop prevents exploding gradients by using adaptive learning rates and running averages of second order gradients.', 'duration': 28.602, 'max_score': 9599.173, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY9599173.jpg'}, {'end': 10005.168, 'src': 'embed', 'start': 9980.563, 'weight': 4, 'content': [{'end': 9986.108, 'text': 'So for scenarios where SGD with momentum would have been better, Adam will incorporate this.', 'start': 9980.563, 'duration': 5.545}, {'end': 9992.395, 'text': 'And where it would be beneficial to use RMSPROP, then Adam will again add the other optimization algorithms.', 'start': 9986.469, 'duration': 5.926}, {'end': 9999.362, 'text': "And that's why ADAM has been proven across the entire industry to be very effective,", 'start': 9992.995, 'duration': 6.367}, {'end': 10005.168, 'text': 'and people are using it across the industry for a wide range of problems and applications.', 'start': 9999.362, 'duration': 5.806}], 'summary': 'Adam optimization algorithm proven effective across industry.', 'duration': 24.605, 'max_score': 9980.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY9980563.jpg'}, {'end': 10130.91, 'src': 'embed', 'start': 10104.065, 'weight': 2, 'content': [{'end': 10112.173, 'text': 'to ensure that the quality is in place and the efficiency is in place and the optimization algorithm is able to handle different scenarios.', 'start': 10104.065, 'duration': 8.108}, {'end': 10118.54, 'text': 'The efficiency with sparse gradients is another advantage of Adam Optimizer,', 'start': 10113.254, 'duration': 5.286}, {'end': 10123.304, 'text': 'because Adam is particularly efficient when dealing with this sparse gradient.', 'start': 10118.54, 'duration': 4.764}, {'end': 10130.91, 'text': "And the other advantage of Adam is that it's able to balance the speed and the stability.", 'start': 10124.445, 'duration': 6.465}], 'summary': 'Adam optimizer ensures quality, efficiency, and handles sparse gradients efficiently.', 'duration': 26.845, 'max_score': 10104.065, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY10104065.jpg'}, {'end': 10308.893, 'src': 'embed', 'start': 10283.081, 'weight': 0, 'content': [{'end': 10292.232, 'text': 'Then Adam has shown in the industry when training different deep neural network to not generalize very well and to solve this problem of overfitting.', 'start': 10283.081, 'duration': 9.151}, {'end': 10295.115, 'text': "So that's exactly what Adam-W tries to do.", 'start': 10292.612, 'duration': 2.503}, {'end': 10305.549, 'text': 'It tries to address the specific problem of Adam such that we can have improved generalization and it can address the shortcomings of the traditional Adam,', 'start': 10295.216, 'duration': 10.333}, {'end': 10308.893, 'text': 'making it one of the best algorithms out there.', 'start': 10305.549, 'duration': 3.344}], 'summary': "Adam-w aims to improve generalization and address adam's overfitting, making it one of the best algorithms.", 'duration': 25.812, 'max_score': 10283.081, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY10283081.jpg'}], 'start': 9211.753, 'title': 'Optimization algorithms in neural networks', 'summary': 'Discusses the risks of overfitting, adaptive learning rate, and optimization algorithms such as adam, rmsprop, and adagrad, addressing issues like vanishing and exploding gradients, and introducing solutions like adamw to improve generalization and performance on pre-trained models.', 'chapters': [{'end': 9258.537, 'start': 9211.753, 'title': 'Haitian usage and overfitting', 'summary': "Discusses the risk of overfitting in using second order partial derivative for estimating gradients, leading to memorization of training data and hindering the model's ability to generalize well on unseen data.", 'duration': 46.784, 'highlights': ['The risk of overfitting when using second order partial derivative for estimating gradients', 'Model memorizing training data and overly relying on it for parameter updates', "Hindering the model's ability to generalize well on unseen data"]}, {'end': 9572.249, 'start': 9258.717, 'title': 'Adaptive learning rate in neural networks', 'summary': 'Discusses the concept of adaptive learning rate in neural networks, its impact on model performance, and provides examples of popular adaptive optimization algorithms such as adam, rmsprop, and adagrad.', 'duration': 313.532, 'highlights': ['The impact of adaptive learning rate on deep learning model performance, including its ability to prevent stagnation, reduce oscillations, and avoid getting stuck in local minimums.', 'Examples of popular adaptive optimization algorithms such as ADAM, RMSPROP, and AdaGrad, with a mention of their performance improvement in optimization tasks.', 'Description of the concept of adaptive learning rate in neural networks, including the different approach compared to constant learning rates and the importance of adjusting the learning rate based on training data and features.', 'Explanation of the ADAM optimization algorithm as an adaptive moment estimation algorithm that adapts the learning rate accordingly and is known for its improved performance in optimization tasks.', 'Description of the RMSPROP algorithm as an adaptive optimization process that addresses some of the shortcomings of the traditional gradient descent algorithm, aiming to minimize the loss function of deep learning models.']}, {'end': 10005.168, 'start': 9573.009, 'title': 'Rms prop and adam in neural networks', 'summary': 'Explains the concepts of rms prop and adam as adaptive optimization algorithms that address the vanishing and exploding gradient problems by using adaptive learning rates and combining the benefits of momentum from sgd and the running average of second-order derivatives.', 'duration': 432.159, 'highlights': ['RMS Prop uses an adaptive learning rate to control the exploding and vanishing gradient problems by adjusting the learning rate based on a running average of second-order gradients, effectively reducing the learning rate for parameters with large gradients and increasing it for parameters with small gradients, thus stabilizing the optimization process.', "ADAM combines the benefits of momentum from SGD and the running average of second-order derivatives from RMS Prop to adapt the learning rate and improve the optimization algorithm's quality, making it effective across different scenarios and widely used in the industry."]}, {'end': 10449.99, 'start': 10005.968, 'title': 'Understanding adam and adamw', 'summary': 'Explains the adam optimization algorithm, highlighting its benefits such as efficiency with sparse gradients, speed, stability, and robustness, and introduces adamw as a solution to the overfitting problem faced by traditional adam, improving generalization and performance on pre-trained models.', 'duration': 444.022, 'highlights': ['Adam offers benefits from multiple optimization algorithms, including efficiency with sparse gradients, faster convergence to the global optimum compared to SGD, and stability in controlling the learning process.', 'AdamW addresses the overfitting problem faced by traditional Adam by incorporating a decay penalization term in the update process, proving to have a much better impact on the generalization of the model.', 'AdamW is preferred over Adam in fine-tuning pre-trained models, solving the problem of overfitting and ensuring improved generalization.', 'Adam adjusts the momentum and velocity by dividing MT and vt to 1 minus beta 1T and 1 minus beta 2 to the power t, respectively, to correct for biases and adapt the learning rate.']}], 'duration': 1238.237, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY9211753.jpg', 'highlights': ['AdamW addresses overfitting problem faced by traditional Adam, proving better generalization', 'RMS Prop uses adaptive learning rate to control exploding and vanishing gradient problems', 'Adam offers efficiency with sparse gradients, faster convergence, and stability in learning process', 'Adaptive learning rate impacts deep learning model performance, preventing stagnation and reducing oscillations', 'ADAM combines momentum from SGD and running average of second-order derivatives from RMS Prop', 'Description of adaptive learning rate in neural networks and its importance in adjusting based on training data', 'Risk of overfitting when using second order partial derivative for estimating gradients', 'Model memorizing training data and hindering generalization on unseen data', 'Explanation of ADAM optimization algorithm as an adaptive moment estimation algorithm']}, {'end': 12050.513, 'segs': [{'end': 10502.338, 'src': 'embed', 'start': 10475.545, 'weight': 0, 'content': [{'end': 10480.708, 'text': 'especially when we have deep neural networks or when we are firetuning pre-trained model.', 'start': 10475.545, 'duration': 5.163}, {'end': 10487.372, 'text': 'And this also means that the algorithm will perform equally well on an unseen data.', 'start': 10481.409, 'duration': 5.963}, {'end': 10496.676, 'text': "Unlike when we are using the traditional ADAM, which in some scenarios doesn't perform the neuralization very well and it suffers from overfitting.", 'start': 10488.072, 'duration': 8.604}, {'end': 10502.338, 'text': 'The next question is what is batch normalization and why it is used in neural networks?', 'start': 10497.136, 'duration': 5.202}], 'summary': 'Improves algorithm performance on unseen data, unlike traditional adam, which may suffer from overfitting.', 'duration': 26.793, 'max_score': 10475.545, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY10475545.jpg'}, {'end': 10838.788, 'src': 'embed', 'start': 10812.851, 'weight': 1, 'content': [{'end': 10817.335, 'text': "So in each layer, we don't have entirely different distribution for activations.", 'start': 10812.851, 'duration': 4.484}, {'end': 10824.861, 'text': 'And when we have similar distributions and this is just normal, then we will have more stable learning process.', 'start': 10817.575, 'duration': 7.286}, {'end': 10832.945, 'text': 'And this will then allow for higher learning rates, which will then accelerate the training process of a neural network.', 'start': 10825.561, 'duration': 7.384}, {'end': 10838.788, 'text': 'And when we are accelerating the training process, we are making a lesser amount of these oscillations,', 'start': 10833.285, 'duration': 5.503}], 'summary': 'Similar distributions in each layer lead to stable learning and higher learning rates, accelerating neural network training and reducing oscillations.', 'duration': 25.937, 'max_score': 10812.851, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY10812851.jpg'}, {'end': 11143.148, 'src': 'embed', 'start': 11111.551, 'weight': 2, 'content': [{'end': 11116.238, 'text': 'And layer normalization has proven to be much more effective in those cases.', 'start': 11111.551, 'duration': 4.687}, {'end': 11126.072, 'text': 'So therefore, for this different sorts of architectures, we can then use layer normalization whenever it is not possible to use batch normalization.', 'start': 11116.579, 'duration': 9.493}, {'end': 11136.682, 'text': 'Layer normalization is largely used also in the state-of-the-art transformers, which are a part of the trendy large language models such as GPTs,', 'start': 11126.773, 'duration': 9.909}, {'end': 11143.148, 'text': 'which are encoder-only, but also the decoder-only architectures such as BERT.', 'start': 11136.682, 'duration': 6.466}], 'summary': 'Layer normalization is effective in different architectures, used in state-of-the-art transformers like gpts and bert.', 'duration': 31.597, 'max_score': 11111.551, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY11111551.jpg'}, {'end': 11219.604, 'src': 'embed', 'start': 11193.127, 'weight': 3, 'content': [{'end': 11197.89, 'text': 'So, residual connections have a very important function in neural networks.', 'start': 11193.127, 'duration': 4.763}, {'end': 11206.615, 'text': 'And specifically, they try to optimize the performance of neural networks by combating the infamous vanishing gradient problem.', 'start': 11198.13, 'duration': 8.485}, {'end': 11208.276, 'text': 'The residual connections.', 'start': 11206.835, 'duration': 1.441}, {'end': 11219.604, 'text': 'they play a key role in deep learning architectures like ResNet transformers, the DGPT series, large language models, a SWERT model.', 'start': 11208.276, 'duration': 11.328}], 'summary': 'Residual connections optimize neural networks by addressing vanishing gradient problem in architectures like resnet, transformers, dgpt series, and swert model.', 'duration': 26.477, 'max_score': 11193.127, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY11193127.jpg'}, {'end': 11879.138, 'src': 'embed', 'start': 11853.536, 'weight': 4, 'content': [{'end': 11863.859, 'text': 'then we will be clipping that gradient to ensure that this gradient is not too large and we are not updating our weight parameters or bias factors too much.', 'start': 11853.536, 'duration': 10.323}, {'end': 11873.413, 'text': 'So, in this way, what we are doing is that we are kind of stabilizing our neural network, which is especially important for architectures like LSTMs,', 'start': 11864.966, 'duration': 8.447}, {'end': 11879.138, 'text': 'RNNs, GRUs, which have this sequential nature of the data with too many layers.', 'start': 11873.413, 'duration': 5.725}], 'summary': 'Stabilizing neural network by clipping gradient to prevent excessive updates in weight parameters and bias factors.', 'duration': 25.602, 'max_score': 11853.536, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY11853536.jpg'}], 'start': 10449.99, 'title': 'Optimizing model parameters and architectures', 'summary': "Covers the use of modified adam optimizer with learning rate for improved generalizability, reduced overfitting, and better performance, explains batch normalization's impact on stabilizing neural networks and its regularization effect, discusses layer normalization's effectiveness in stabilizing rnns, lstms, and transformers, and addresses the vanishing and exploding gradient problems in deep neural networks.", 'chapters': [{'end': 10496.676, 'start': 10449.99, 'title': 'Optimizing model parameters with learning rate', 'summary': 'Discusses the use of a modified adam optimizer, which directly incorporates the learning rate when updating model parameters, leading to better generalizability, reduced overfitting, and improved performance in scenarios involving deep neural networks or fine-tuning pre-trained models, compared to the traditional adam optimizer.', 'duration': 46.686, 'highlights': ['The modified ADAM optimizer directly incorporates the learning rate when updating model parameters, leading to better generalizability and reduced overfitting.', 'This approach has shown to have a much better impact in terms of solving the overfitting problem and making the entire trained model more generalizable, ensuring better performance in specific scenarios.', 'It is especially beneficial for deep neural networks and fine-tuning pre-trained models, improving performance in these contexts.', 'The traditional ADAM optimizer may not perform neuralization very well in some scenarios and suffers from overfitting.']}, {'end': 11002.331, 'start': 10497.136, 'title': 'Understanding batch normalization', 'summary': 'Explains the concept of batch normalization, its purpose in stabilizing neural networks, and its impacts including stabilizing distribution of activations, reducing sensitivity to weight initialization, and indirect regularization effect, resulting in smoother and quicker convergence towards the global optimum.', 'duration': 505.195, 'highlights': ['Batch normalization normalizes the activations per batch to address the issue of internal covariate shift in neural networks, stabilizing the distribution of activations, and allowing for higher learning rates and smoother training process.', 'The use of batch normalization reduces the sensitivity of the algorithm to weight initialization, increasing the likelihood of finding a global optimum rather than a local one.', 'Batch normalization indirectly acts as a regularization technique by reducing the impact of noise data points, minimizing the risk of overfitting and the need for additional regularization algorithms.']}, {'end': 11422.591, 'start': 11002.431, 'title': 'Layer norm & residual conn', 'summary': 'Discusses the use of layer normalization and its effectiveness in stabilizing neural networks, particularly in rnns, lstms, and transformers, and the function of residual connections in combating the vanishing gradient problem in deep learning architectures.', 'duration': 420.16, 'highlights': ['Layer normalization is more effective than batch normalization in certain neural network architectures such as RNNs, LSTMs, and transformers, particularly when dealing with varying or small batch sizes.', 'Residual connections play a crucial role in combating the vanishing gradient problem in deep learning architectures like ResNet transformers and large language models, by adding a shortcut or skip connection that directly contributes to the final outcome.']}, {'end': 12050.513, 'start': 11422.591, 'title': 'Residual connections & gradient clipping', 'summary': 'Discusses the vanishing gradient problem, the role of residual connections in solving it, and the concept of gradient clipping to address the exploding gradient problem, emphasizing the impact on deep neural networks like rnns and lstms.', 'duration': 627.922, 'highlights': ['Residual connections provide a shortcut for the gradient flow, reducing the vanishing gradient problem and allowing gradients to skip some layers, ultimately solving the issue (e.g., y = x + f, enabling the gradient to flow directly through the shortcut).', 'Gradient clipping addresses the exploding gradient problem by limiting gradients above a certain threshold, stabilizing the neural network and preventing erratic behavior, particularly crucial for architectures with sequential data and numerous layers (e.g., LSTMs, RNNs).', 'Caviar initialization aims to maintain consistent variance of activations and gradients across layers by setting initial weights based on the number of input and output neurons, using statistical distributions like the uniform distribution with specific parameters to stabilize the network.']}], 'duration': 1600.523, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY10449990.jpg', 'highlights': ['The modified ADAM optimizer improves generalizability and reduces overfitting', 'Batch normalization stabilizes activations, allowing for higher learning rates', 'Layer normalization is more effective in certain architectures like RNNs and LSTMs', 'Residual connections combat the vanishing gradient problem in deep learning', 'Gradient clipping stabilizes the neural network and prevents erratic behavior']}, {'end': 13165.314, 'segs': [{'end': 12125.826, 'src': 'embed', 'start': 12101.851, 'weight': 0, 'content': [{'end': 12112.096, 'text': "And that's why caviar initialization can be so important and so significant because it can help to stabilize the entire network.", 'start': 12101.851, 'duration': 10.245}, {'end': 12118.659, 'text': 'It can bring consistency into the variance of these activations and the gradients across different layers.', 'start': 12112.196, 'duration': 6.463}, {'end': 12125.826, 'text': 'and it will also reduce this risk of vanishing and exploding gradients, which will promote stability,', 'start': 12119.219, 'duration': 6.607}], 'summary': 'Caviar initialization stabilizes network, reduces risk of vanishing/exploding gradients.', 'duration': 23.975, 'max_score': 12101.851, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY12101851.jpg'}, {'end': 12206.134, 'src': 'embed', 'start': 12181.925, 'weight': 1, 'content': [{'end': 12194.686, 'text': 'These activation functions can help you solve the vanishing gradient problem and ensure that your gradients will not vanish in those deep networks by the time you come from these very deep layers to the early layers.', 'start': 12181.925, 'duration': 12.761}, {'end': 12206.134, 'text': "So don't use the sigmoid, don't use the tang, but use the leaky ReLU or ReLU in order to combat the vanishing gradient problem.", 'start': 12196.685, 'duration': 9.449}], 'summary': 'Use leaky relu or relu to combat vanishing gradient problem in deep networks.', 'duration': 24.209, 'max_score': 12181.925, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY12181925.jpg'}, {'end': 12291.602, 'src': 'embed', 'start': 12261.905, 'weight': 2, 'content': [{'end': 12265.05, 'text': 'in all those cases you can use residual connections.', 'start': 12261.905, 'duration': 3.145}, {'end': 12269.854, 'text': 'So residual connections will help you to open the door for the shortcut,', 'start': 12265.33, 'duration': 4.524}, {'end': 12276.421, 'text': 'for your gradients to flip through your network without going through all these transformations.', 'start': 12269.854, 'duration': 6.567}, {'end': 12281.646, 'text': 'And this will then, on its turn, help you reduce the risk of vanishing gradients.', 'start': 12276.681, 'duration': 4.965}, {'end': 12291.602, 'text': 'Then the final way of combating the vanishing gradient problem is by using an appropriate architecture.', 'start': 12284.158, 'duration': 7.444}], 'summary': 'Residual connections prevent vanishing gradients, reducing risk by providing shortcuts through the network and an appropriate architecture.', 'duration': 29.697, 'max_score': 12261.905, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY12261905.jpg'}, {'end': 12592.805, 'src': 'embed', 'start': 12569.247, 'weight': 3, 'content': [{'end': 12578.814, 'text': 'so dropout is a regularization technique commonly used in deep learning, specifically in order to solve the problem of overfitting.', 'start': 12569.247, 'duration': 9.567}, {'end': 12588.602, 'text': 'so we just saw in the previous interview question that overfitting can cause a lot of problems when training neural networks, and we want to have,', 'start': 12578.814, 'duration': 9.788}, {'end': 12592.805, 'text': 'ideally, a model that generalizes well on an unseen data,', 'start': 12588.602, 'duration': 4.203}], 'summary': 'Dropout is a common deep learning regularization technique to address overfitting.', 'duration': 23.558, 'max_score': 12569.247, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY12569247.jpg'}, {'end': 12935.655, 'src': 'embed', 'start': 12912.661, 'weight': 4, 'content': [{'end': 12925.013, 'text': 'they aim to improve the robustness of your model and they reduce the chance and the risk of overfitting by introducing randomness and diversity in the training process.', 'start': 12912.661, 'duration': 12.352}, {'end': 12935.655, 'text': 'You might recall the random forest algorithm, which is an ensemble machine learning algorithm, can be used for both classification and for regression.', 'start': 12926.445, 'duration': 9.21}], 'summary': 'Improving model robustness, reducing overfitting using randomness and diversity. random forest for classification and regression.', 'duration': 22.994, 'max_score': 12912.661, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY12912661.jpg'}], 'start': 12050.513, 'title': 'Neural network variances', 'summary': 'Covers the significance of constant variance in neural network weights, addressing vanishing and exploding gradient problems through caviar initialization, appropriate activation functions, batch normalization, residual connections, and weight initialization techniques, as well as the use of dropout and random forest to prevent overfitting.', 'chapters': [{'end': 12125.826, 'start': 12050.513, 'title': 'Variance in neural networks', 'summary': 'Explains the importance of constant variance in neural network weights to combat vanishing and exploding gradient problems, with caviar initialization being significant in stabilizing the network and promoting stability.', 'duration': 75.313, 'highlights': ['Caviar initialization is important for stabilizing the entire network, promoting stability, and reducing the risk of vanishing and exploding gradients.', 'Constant variance in weight means that the weight will always change with a similar amount, preventing too high or too low gradients, and combating the vanishing gradient problem.', 'The variance of the random distribution with uniform distribution will be equal to 2 divided by the square root of n in plus n out.']}, {'end': 12334.827, 'start': 12125.826, 'title': 'Solving vanishing gradient problem in deep learning', 'summary': 'Discusses various approaches to combat the vanishing gradient problem, such as using appropriate activation functions like relu, employing techniques like cavier initialization and batch normalization, utilizing residual connections, and selecting appropriate architectures.', 'duration': 209.001, 'highlights': ['Using appropriate activation functions like ReLU or leaky ReLU can combat the vanishing gradient problem by preventing saturation and ensuring consistent gradients.', 'Cavier initialization and batch normalization are effective techniques in combating the vanishing gradient problem, introducing stability and consistent gradients.', 'Residual connections in sequence-based architectures such as RNNs, LSTMs, and GRUs help in preventing vanishing gradients by providing shortcuts for gradients to propagate through the network without going through extensive transformations.', 'Selecting appropriate architectures, like transformer architecture or using Adam V, can also help combat the vanishing gradient problem by automatically adding residual connections, layer normalizations, and regularizing the network.']}, {'end': 12824.259, 'start': 12335.068, 'title': 'Solving exploding gradients & overfitting in neural networks', 'summary': 'Discusses solutions for exploding gradients including gradient clipping and weight initialization, and explains overfitting in neural networks, its relation to large weights, and the use of dropout as a regularization technique.', 'duration': 489.191, 'highlights': ['The use of dropout as a regularization technique in neural networks, specifically to reduce overfitting, involves randomly deactivating a subset of neurons in each training iteration, with a dropout rate (p) between 0 and 1, where 1-p represents the proportion of neurons that should not be deactivated, helping to reduce the chance of overfitting and improve generalization.', "The explanation of overfitting in neural networks, emphasizing the model's tendency to memorize training data, the impact of large weights making the model sensitive to outliers and noise, and the importance of solving overfitting to ensure generalizability and performance on unseen data.", 'The methods for solving exploding gradients, including gradient clipping to keep gradients below a certain threshold and weight initialization, particularly the use of caviar initialization to maintain weight variance and combat both vanishing and exploding gradient problems.']}, {'end': 13165.314, 'start': 12825.307, 'title': 'Dropout vs random forest', 'summary': 'Discusses how dropout and random forest prevent overfitting in neural networks, with dropout randomly deactivating p percent of neurons during training and encouraging feature redundancy, while random forest introduces randomness in building decision trees to combat overfitting and improve model generalizability.', 'duration': 340.007, 'highlights': ['Dropout prevents overfitting by randomly deactivating p percent of neurons during training, reducing dependency on certain data points, and encouraging feature redundancy, thus helping to generalize the model better.', 'Random forest combats overfitting by introducing randomness in building decision trees, using bootstrapped samples with replacement and randomly selecting features for splitting, resulting in uncorrelated trees and lower variance, thus improving model generalizability.']}], 'duration': 1114.801, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY12050513.jpg', 'highlights': ['Caviar initialization stabilizes the network, reducing vanishing and exploding gradients.', 'Appropriate activation functions like ReLU combat vanishing gradient problem.', 'Residual connections in sequence-based architectures prevent vanishing gradients.', 'Dropout as a regularization technique reduces overfitting and improves generalization.', 'Random forest combats overfitting by introducing randomness in decision trees.']}, {'end': 14333.05, 'segs': [{'end': 13220.189, 'src': 'embed', 'start': 13190.994, 'weight': 0, 'content': [{'end': 13202.698, 'text': 'But this also means that the neurons have smaller and specifically 1-p probability of being activated during the training process.', 'start': 13190.994, 'duration': 11.704}, {'end': 13210.221, 'text': 'So we are reducing the probability of a neuron to be selected for being activated.', 'start': 13203.479, 'duration': 6.742}, {'end': 13220.189, 'text': 'And this will then introduce inconsistency when it comes to the testing process because we are applying the dropout only during the training.', 'start': 13211.341, 'duration': 8.848}], 'summary': 'Using 1-p probability for neuron activation reduces inconsistency in testing.', 'duration': 29.195, 'max_score': 13190.994, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY13190994.jpg'}, {'end': 13494.618, 'src': 'embed', 'start': 13469.974, 'weight': 1, 'content': [{'end': 13476.699, 'text': "it will also ensure that the network doesn't heavily rely on certain neurons,", 'start': 13469.974, 'duration': 6.725}, {'end': 13486.189, 'text': 'And this will then ensure that your model is not overfitting and not memorizing training data, which might also include noise and outlier points.', 'start': 13477.299, 'duration': 8.89}, {'end': 13494.618, 'text': 'Now, what is the difference between L2 and L1 regularization approaches? So I did briefly mention the difference of the two.', 'start': 13486.769, 'duration': 7.849}], 'summary': 'Regularization prevents overfitting by diversifying neuron reliance.', 'duration': 24.644, 'max_score': 13469.974, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY13469974.jpg'}, {'end': 13634.736, 'src': 'embed', 'start': 13611.58, 'weight': 3, 'content': [{'end': 13618.562, 'text': 'And in some cases, it sets certain weights and specifically those small weights exactly equal to zero.', 'start': 13611.58, 'duration': 6.982}, {'end': 13628.714, 'text': "So in this way, L1 regularization punishes these small weights, so it's heavily shrinking those weights and set them equal to exactly zero.", 'start': 13619.163, 'duration': 9.551}, {'end': 13634.736, 'text': 'And it has less of a punishment effect and is less harsh on these large weights.', 'start': 13629.374, 'duration': 5.362}], 'summary': 'L1 regularization shrinks small weights to zero, less harsh on large weights.', 'duration': 23.156, 'max_score': 13611.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY13611580.jpg'}, {'end': 13728.577, 'src': 'embed', 'start': 13701.744, 'weight': 2, 'content': [{'end': 13708.149, 'text': 'So it sets certain weights equal to zero, which means that it also automatically performs feature selection,', 'start': 13701.744, 'duration': 6.405}, {'end': 13713.493, 'text': 'and it then reduces the dimension of the model and makes the network sparser.', 'start': 13708.149, 'duration': 5.344}, {'end': 13718.053, 'text': 'Whereas in case of L2 or rich regularization,', 'start': 13714.431, 'duration': 3.622}, {'end': 13728.577, 'text': 'the sparsity is low because the regularization process is not performing feature selection and it has no zero weights.', 'start': 13718.053, 'duration': 10.524}], 'summary': 'L1 regularization automatically performs feature selection and reduces model dimension, making the network sparser.', 'duration': 26.833, 'max_score': 13701.744, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY13701744.jpg'}, {'end': 14019.134, 'src': 'embed', 'start': 13999.39, 'weight': 4, 'content': [{'end': 14012.133, 'text': 'because deep learning models are able to counter this course of dimensionality by learning this useful feature representations and reducing this data dimension that we have,', 'start': 13999.39, 'duration': 12.743}, {'end': 14019.134, 'text': 'applying regularization and also using architecture specifically designed for high dimensional data.', 'start': 14012.133, 'duration': 7.001}], 'summary': 'Deep learning models counter dimensionality by learning useful feature representations and reducing data dimension, applying regularization and using architecture designed for high dimensional data.', 'duration': 19.744, 'max_score': 13999.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY13999390.jpg'}], 'start': 13165.414, 'title': 'Dropout, regularization, and curse of dimensionality', 'summary': 'Covers the impact of dropout on training and testing, l1 and l2 regularization techniques, differences between l1 and l2 regularization in neural networks, and the curse of dimensionality in machine learning, addressing computational challenges, overfitting, and the role of generative models.', 'chapters': [{'end': 13541.583, 'start': 13165.414, 'title': 'Dropout impact and regularization techniques', 'summary': 'Discusses the impact of dropout on training and testing, where dropout reduces the activation probability of neurons and requires activation scaling during testing, as well as l1 and l2 regularization techniques to prevent overfitting by shrinking or setting weights and preventing the model from memorizing training data.', 'duration': 376.169, 'highlights': ['Dropout reduces the activation probability of neurons during training, which introduces inconsistency in the testing process and requires scaling the activations by 1-p during testing, with a 20% dropout rate resulting in 80% activation probability during training and requiring scaling by 0.8 during testing.', 'L1 and L2 regularizations are shrinkage techniques used to prevent overfitting by adding penalization factors based on the absolute value of weights (L1) or the squared weights (L2), with L1 allowing feature selection by setting weights to zero and L2 preventing exploding gradients and heavy reliance on certain neurons by shrinking weights towards zero.', 'L2 regularization adds squared weight values to the loss function, known as L2 norm, and impacts the weights by shrinking them towards zero, preventing exploding gradients and heavy reliance on certain neurons.']}, {'end': 13833.82, 'start': 13541.583, 'title': 'L1 vs l2 regularization in neural networks', 'summary': 'Discusses the differences between l1 and l2 regularization in neural networks, highlighting their impact on weight penalizations, feature selection, sparsity, and smoothing process.', 'duration': 292.237, 'highlights': ['L1 regularization performs feature selection by setting certain weights exactly equal to zero, leading to high sparsity, while L2 regularization does not perform feature selection, resulting in low sparsity.', 'L2 regularization heavily penalizes large weights, leading to smaller non-zero weights, while L1 regularization is less harsh on large weights, effectively removing features from the model by setting certain weights to zero.', 'L2 regularization ensures a smoother process by proportionally spreading the error over all weights, while L1 regularization is harsh on small weights, resulting in a less smooth process.']}, {'end': 14333.05, 'start': 13834.44, 'title': 'Curse of dimensionality in ml & generative models', 'summary': 'Discusses the curse of dimensionality in machine learning, where high-dimensional data leads to computational challenges, data sparsity, overfitting, and reduced generalizability, and also explains how deep learning models counter this curse through feature learning, regularization, and architecture designed for high-dimensional data. it also outlines generative models as tools for understanding underlying data distribution, generating new data instances, and performing unsupervised learning tasks.', 'duration': 498.61, 'highlights': ['Deep learning models counter the curse of dimensionality by learning useful feature representations, reducing data dimension, applying regularization, and using architecture specifically designed for high-dimensional data, unlike traditional machine learning models that need feature selection and dimensionality reduction techniques. (Relevance: 5)', 'Generative models aim to model how data is generated, learn the joint probability distribution of features and labels, and are particularly useful for understanding underlying data distribution, generating new data instances, and performing unsupervised learning tasks such as clustering, outlier detection, dimensionality reduction, and data generation. (Relevance: 4)', 'The curse of dimensionality in machine learning leads to computational challenges, data sparsity, overfitting, and reduced generalizability when dealing with high-dimensional data, affecting the performance of distance-based models like KNN or k-means, and necessitating feature selection and dimensionality reduction techniques. (Relevance: 3)']}], 'duration': 1167.636, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/BAregq0sdyY/pics/BAregq0sdyY13165414.jpg', 'highlights': ['Dropout reduces neuron activation probability during training, requiring scaling by 1-p during testing.', 'L2 regularization prevents exploding gradients and heavy reliance on certain neurons.', 'L1 regularization performs feature selection by setting weights to zero, leading to high sparsity.', 'L2 regularization heavily penalizes large weights, leading to smaller non-zero weights.', 'Deep learning models counter the curse of dimensionality by learning useful feature representations.']}], 'highlights': ['Tadev from LearnerTech teaches a deep learning interview preparation course addressing 50 common interview questions crucial for data science, machine learning, AI, and research scientist interviews.', 'Comprehensive coverage of deep learning concepts, including basic and advanced topics, with a focus on preparing for deep learning interviews and follow-up questions.', 'Detailed explanation of various optimization algorithms, including SGD, momentum, BatchGD, MiniBatchGD, Haitian, RMS prop, and Adam algorithm, crucial for deep learning interviews.', 'Deep learning forms the cornerstone of large language models and generative AI, requiring knowledge of linear algebra, mathematics, differentiation theory, and advanced algorithms.', 'The core of neural network is made up of neurons forming layers, including input layer and one or more hidden layers, which help in learning and understanding different patterns and transforming information.', 'Deep learning involves training artificial neural networks on large amounts of data to identify and learn hidden patterns and nonlinear relationships, which traditional machine learning models like linear regression or random forest are not capable of.', "The process involves continuously activating neurons, computing the overall cost function, and using gradients to update weights and parameters to minimize error in the model's predictions.", 'In-depth understanding of key concepts like vanishing gradient problem, exploding gradient problem, batch normalization, layer normalization, residual connections, gradient clipping, and overfitting.', 'The activation functions introduce non-linearity, crucial for uncovering complex, hidden patterns in data.', 'The weights assigned to input features determine their contribution to hidden units, enabling the understanding of their impact on the hidden layer.', 'The leaky ReLU activation function activates negative values at a lesser extreme, recommended for hidden layers.', 'The ReLU and Leaky ReLU activation functions prevent vanishing gradient problem.', 'The sigmoid and tanh activation functions are recommended for output layers.', 'The importance of optimizing hyperparameters for proper learning of dependencies.', 'The explanation of gradient clipping as a solution to the exploding gradient problem.', 'Stabilizing neural networks by not updating weight parameters or bias factors too much is important for architectures like LSTMs, RNNs, GRUs, ensuring a stable network that properly learns interdependencies.', 'The multi-class cross entropy, also known as softmax function, is used for multi-class classification and measures the performance of a model that wants to classify observations to one of the multiple classes.', 'The batch gradient descent uses the entire training data, providing high-quality and stable updates, but is inefficient and slow, especially with large and complex datasets.', 'The mini-batch gradient descent strikes a balance between batch gradient descent and stochastic gradient descent by using randomly sampled training observations in batches, resulting in improved optimization quality and consistency of updates.', 'AdamW addresses overfitting problem faced by traditional Adam, proving better generalization', 'RMS Prop uses adaptive learning rate to control exploding and vanishing gradient problems', 'Adam offers efficiency with sparse gradients, faster convergence, and stability in learning process', 'The modified ADAM optimizer improves generalizability and reduces overfitting', 'Batch normalization stabilizes activations, allowing for higher learning rates', 'Layer normalization is more effective in certain architectures like RNNs and LSTMs', 'Residual connections combat the vanishing gradient problem in deep learning', 'Caviar initialization stabilizes the network, reducing vanishing and exploding gradients.', 'Appropriate activation functions like ReLU combat vanishing gradient problem.', 'Residual connections in sequence-based architectures prevent vanishing gradients.', 'Dropout as a regularization technique reduces overfitting and improves generalization.', 'L2 regularization prevents exploding gradients and heavy reliance on certain neurons.', 'Deep learning models counter the curse of dimensionality by learning useful feature representations.']}