title

Gradient Descent For Neural Network | Deep Learning Tutorial 12 (Tensorflow2.0, Keras & Python)

description

Gradient descent is the heart of all supervised learning models. It is important to understand this technique if you are pursuing a career as a data scientist or a machine learning engineer. In this video we will see a very simple explanation of what a gradient descent is for a neural network or a logistic regression (remember logistic regression is a very simple single neuron neural network). We will than implement gradient descent from scratch in python.
In my machine learning tutorial series I already have a video on gradient descent but that one is on linear regression whereas this video is for logistic regression for neural network. Here is the link of my linear regression GD video,
GD tutorial on regression: https://www.youtube.com/watch?v=vsWrXfO3wWw
Code of this tutorial: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/master/6_gradient_descent/6_gradient_descent.ipynb
Do you want to learn technology from me? Check https://codebasics.io/ for my affordable video courses.
ðŸ”– Hashtags ðŸ”–
#gradientdescent #gradientdescentneuralnetwork #gradientdescentdeeplearning #gradientdescentalgorithm #gradientdescentpython
Next video: https://www.youtube.com/watch?v=PQCE9ChuIDY&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=13
Previous video: https://www.youtube.com/watch?v=E1yyaLRUnLo&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=11
Deep learning playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO
Machine learning playlist :Â https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rwÂ Â
Prerequisites for this series:Â Â
1: Python tutorials (first 16 videos):Â https://www.youtube.com/playlist?list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0Â Â Â
2: Pandas tutorials(first 8 videos): https://www.youtube.com/playlist?list=PLeo1K3hjS3uuASpe-1LjfG5f14Bnozjwy
3: Machine learning playlist (first 16 videos):Â https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rwÂ Â
ðŸŒŽ My Website For Video Courses: https://codebasics.io/
Need help building software or data analytics and AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website.
Facebook: https://www.facebook.com/codebasicshub
Twitter: https://twitter.com/codebasicshub
Patreon: https://www.patreon.com/codebasics

detail

{'title': 'Gradient Descent For Neural Network | Deep Learning Tutorial 12 (Tensorflow2.0, Keras & Python)', 'heatmap': [{'end': 524.169, 'start': 490.369, 'weight': 0.73}, {'end': 803.706, 'start': 719.323, 'weight': 0.969}, {'end': 1305.707, 'start': 1215.79, 'weight': 1}], 'summary': 'This tutorial covers the importance of gradient descent in supervised machine learning, linear relationship finding, derivatives in neural networks, insurance prediction using tensorflow, training and implementing neural networks, and implementing gradient descent in python, achieving an accuracy of 0.7054 and a loss of 0.4200.', 'chapters': [{'end': 85.663, 'segs': [{'end': 43.174, 'src': 'embed', 'start': 0.549, 'weight': 1, 'content': [{'end': 4.39, 'text': 'Gradient descent is at the heart of supervised machine learning.', 'start': 0.549, 'duration': 3.841}, {'end': 11.092, 'text': 'It could be statistical machine learning or deep learning and in this video we are going to cover exactly this topic.', 'start': 4.45, 'duration': 6.642}, {'end': 16.273, 'text': "So we'll go through some theory first and then we'll write Python code to implement gradient descent.", 'start': 11.292, 'duration': 4.981}, {'end': 20.795, 'text': 'Now, if you have seen my machine learning tutorial series,', 'start': 16.773, 'duration': 4.022}, {'end': 29.543, 'text': 'previously I had a separate gradient descent tutorial that was specifically built for a regression problem for housing price prediction.', 'start': 20.795, 'duration': 8.748}, {'end': 33.907, 'text': 'In this video we are going to cover some of the same theory,', 'start': 30.184, 'duration': 3.723}, {'end': 43.174, 'text': 'but we will build gradient descent for a simple neural network or a logistic regression for an insurance data set.', 'start': 33.907, 'duration': 9.267}], 'summary': 'Gradient descent for machine learning, including python code implementation and neural network application.', 'duration': 42.625, 'max_score': 0.549, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs549.jpg'}, {'end': 92.005, 'src': 'embed', 'start': 61.669, 'weight': 0, 'content': [{'end': 64.331, 'text': "So it's very important that you understand the math behind it.", 'start': 61.669, 'duration': 2.662}, {'end': 70.918, 'text': 'You understand how the technique works because it is, as I mentioned earlier, at the heart of machine learning.', 'start': 64.37, 'duration': 6.548}, {'end': 73.72, 'text': 'especially supervised machine learning.', 'start': 71.9, 'duration': 1.82}, {'end': 75.621, 'text': 'So gradient descent is everywhere.', 'start': 73.8, 'duration': 1.821}, {'end': 79.282, 'text': 'It is the core of ML domain.', 'start': 75.861, 'duration': 3.421}, {'end': 81.782, 'text': "So that's what we will cover today.", 'start': 80.062, 'duration': 1.72}, {'end': 83.343, 'text': "Let's get started.", 'start': 82.643, 'duration': 0.7}, {'end': 85.663, 'text': "Let's start with a little quiz here.", 'start': 83.923, 'duration': 1.74}, {'end': 92.005, 'text': 'I have x and y values and I want you to figure out the relationship between the two.', 'start': 86.224, 'duration': 5.781}], 'summary': 'Understanding the math behind gradient descent is crucial for supervised machine learning, as it is at the core of the ml domain.', 'duration': 30.336, 'max_score': 61.669, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs61669.jpg'}], 'start': 0.549, 'title': 'Understanding gradient descent in ml', 'summary': 'Covers the importance of gradient descent in supervised machine learning, emphasizing its significance in statistical machine learning and deep learning, and its relevance to data scientist and machine learning engineer roles.', 'chapters': [{'end': 85.663, 'start': 0.549, 'title': 'Understanding gradient descent in ml', 'summary': 'Covers the importance of gradient descent in supervised machine learning, emphasizing its significance in statistical machine learning and deep learning, and its relevance to data scientist and machine learning engineer roles.', 'duration': 85.114, 'highlights': ['Gradient descent is crucial in supervised machine learning, especially in statistical and deep learning, and is essential for data scientist and machine learning engineer roles.', 'The video explains the theory of gradient descent and demonstrates Python code implementation for a simple neural network or logistic regression for an insurance dataset.', 'Understanding the math and working of gradient descent is vital for roles in the ML domain, as it forms the core of machine learning, especially in supervised machine learning.']}], 'duration': 85.114, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs549.jpg', 'highlights': ['Understanding the math and working of gradient descent is vital for roles in the ML domain, as it forms the core of machine learning, especially in supervised machine learning.', 'Gradient descent is crucial in supervised machine learning, especially in statistical and deep learning, and is essential for data scientist and machine learning engineer roles.', 'The video explains the theory of gradient descent and demonstrates Python code implementation for a simple neural network or logistic regression for an insurance dataset.']}, {'end': 616.696, 'segs': [{'end': 141.489, 'src': 'embed', 'start': 86.224, 'weight': 2, 'content': [{'end': 92.005, 'text': 'I have x and y values and I want you to figure out the relationship between the two.', 'start': 86.224, 'duration': 5.781}, {'end': 102.113, 'text': 'So the function that describes the relationship between these two variables will be y is equal to x into 2.', 'start': 92.825, 'duration': 9.288}, {'end': 104.976, 'text': 'All right, how about this? Pause this video and figure it out.', 'start': 102.113, 'duration': 2.863}, {'end': 110.801, 'text': 'What is the linear equation for this table? Well, this one is also easy.', 'start': 105.096, 'duration': 5.705}, {'end': 113.203, 'text': 'Y is equal to X by X into three.', 'start': 111.201, 'duration': 2.002}, {'end': 120.11, 'text': 'How about this? Okay, again, pause this video and try to figure it out.', 'start': 115.445, 'duration': 4.665}, {'end': 126.589, 'text': "Well this is x into 2 plus 3 because let's take an example.", 'start': 122.145, 'duration': 4.444}, {'end': 131.413, 'text': 'If x is 2, 2 into 2 is 4 plus 3 is 7 so you get 7.', 'start': 127.209, 'duration': 4.204}, {'end': 135.596, 'text': 'x is 3, 3 into 2 is 6 plus 3 is 9 so you get 9.', 'start': 131.413, 'duration': 4.183}, {'end': 137.298, 'text': 'Alright, How about this one?', 'start': 135.596, 'duration': 1.702}, {'end': 141.489, 'text': 'well, this might not be that easy.', 'start': 139.488, 'duration': 2.001}], 'summary': 'The relationship between x and y is expressed through different linear equations, including y=2x, y=3x, and x*2+3.', 'duration': 55.265, 'max_score': 86.224, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs86224.jpg'}, {'end': 197.863, 'src': 'embed', 'start': 171.417, 'weight': 0, 'content': [{'end': 176.44, 'text': 'and this gradient descent is at the core of machine learning and deep learning, especially supervised learning,', 'start': 171.417, 'duration': 5.023}, {'end': 182.844, 'text': 'because in supervised learning you have truth data which is x and y, so you you will often have this type of table.', 'start': 176.44, 'duration': 6.404}, {'end': 190.299, 'text': 'And then gradient descent, all it does is it finds the relationship between these two.', 'start': 183.855, 'duration': 6.444}, {'end': 197.863, 'text': "This is also called a prediction function, so that tomorrow, if we have a new value for x, let's say 11,", 'start': 190.819, 'duration': 7.044}], 'summary': 'Gradient descent is crucial in supervised learning to find the relationship between input and output data.', 'duration': 26.446, 'max_score': 171.417, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs171417.jpg'}, {'end': 524.169, 'src': 'heatmap', 'start': 490.369, 'weight': 0.73, 'content': [{'end': 498.912, 'text': 'So now you sum up all the errors for all the samples, and then you calculate the loss.', 'start': 490.369, 'duration': 8.543}, {'end': 505.348, 'text': 'So here we are using log loss, which is also known as, by the way, binary cross entropy.', 'start': 500.046, 'duration': 5.302}, {'end': 512.792, 'text': 'And that is nothing but sum of all the losses and you take the simple average of it.', 'start': 506.689, 'duration': 6.103}, {'end': 518.227, 'text': 'So After the first epoch.', 'start': 515.273, 'duration': 2.954}, {'end': 519.629, 'text': 'so what is epoch?', 'start': 518.227, 'duration': 1.402}, {'end': 524.169, 'text': 'epoch is going through all your training samples once.', 'start': 519.629, 'duration': 4.54}], 'summary': 'Using log loss for calculating average error after the first epoch.', 'duration': 33.8, 'max_score': 490.369, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs490369.jpg'}, {'end': 573.261, 'src': 'embed', 'start': 539.313, 'weight': 1, 'content': [{'end': 549.006, 'text': 'and then Your goal is to back propagate this loss so that you can adjust this weight 1 and weight 2..', 'start': 539.313, 'duration': 9.693}, {'end': 557.233, 'text': 'Remember that when you train neural network you might have to run multiple epoch until you get a correct value of w1 and w2.', 'start': 549.006, 'duration': 8.227}, {'end': 560.496, 'text': 'So after the first epoch my log loss was 4.31.', 'start': 558.614, 'duration': 1.882}, {'end': 561.056, 'text': 'I need to do something.', 'start': 560.496, 'duration': 0.56}, {'end': 573.261, 'text': 'to now update the weights W1 and W2 in such a way that my log loss can be reduced.', 'start': 564.158, 'duration': 9.103}], 'summary': 'Back propagate loss to adjust weights in neural network, log loss reduced to 4.31 after first epoch.', 'duration': 33.948, 'max_score': 539.313, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs539313.jpg'}], 'start': 86.224, 'title': 'Linear relationships and gradient descent', 'summary': 'Discusses finding linear relationships between x and y values through functions like y = 2x, y = 3x, and x = 2x + 3, providing examples. it also explains gradient descent in machine learning, its use in supervised learning, training neural networks, and calculating log loss.', 'chapters': [{'end': 141.489, 'start': 86.224, 'title': 'Finding linear relationships', 'summary': 'Discusses finding linear relationships between x and y values, presenting functions such as y = 2x, y = 3x, and x = 2x + 3, while providing examples and explanations for each.', 'duration': 55.265, 'highlights': ['The linear equation for the relationship between x and y values is y = 2x, with examples of the relationship demonstrated for different values of x, showing y = 2x + 3 for x = 2 and y = 9 for x = 3.', 'Another linear equation presented is y = 3x, providing a clear example of the relationship between x and y values.', 'The function x = 2x + 3 is introduced, with an example illustrating its application for different values of x.', 'The speaker encourages the audience to pause the video and attempt to deduce the linear equations for the given relationships, promoting interactive learning.']}, {'end': 616.696, 'start': 141.489, 'title': 'Gradient descent in machine learning', 'summary': 'Explains the concept of gradient descent in machine learning, particularly in supervised learning, where the technique is used to find the relationship between two tables, as well as its application in training a neural network through back propagation and the calculation of log loss.', 'duration': 475.207, 'highlights': ['Gradient descent is used to find the linear equation for the relationship between two tables in supervised learning, with a technique called prediction function, which allows for the calculation of y values for new input data, such as in the insurance dataset where it can predict if a person is likely to buy insurance based on age and affordability.', 'The chapter discusses the training process of a neural network using gradient descent, where the calculation of log loss is used to measure the error in prediction, and the goal is to adjust the weights in such a way that the log loss is minimized, demonstrated with sample data and the use of log loss function for error calculation.', 'The concept of back propagation in training a neural network is explained, highlighting the use of multiple epochs to adjust the weights of the network until a correct value is obtained, with the specific goal of reducing log loss through the adjustment of weights.']}], 'duration': 530.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs86224.jpg', 'highlights': ['Gradient descent used to find linear equation in supervised learning, with prediction function for new input data.', 'Training process of neural network using gradient descent, calculating log loss to measure prediction error.', 'Linear equation for relationship between x and y values is y = 2x, with examples demonstrated for different x values.', 'Linear equation presented as y = 3x, providing a clear example of the relationship between x and y values.', 'Function x = 2x + 3 introduced, with an example illustrating its application for different x values.', 'Back propagation in training a neural network explained, using multiple epochs to adjust weights and reduce log loss.', 'Encouragement for interactive learning by pausing the video to deduce linear equations for given relationships.']}, {'end': 854.274, 'segs': [{'end': 688.426, 'src': 'embed', 'start': 617.437, 'weight': 1, 'content': [{'end': 631.701, 'text': 'So derivative of your loss compared to w1 indicates the what it will indicate is how my log loss changes for a given change in W1.', 'start': 617.437, 'duration': 14.264}, {'end': 638.963, 'text': "And that's an important parameter to understand because now you can do something like this.", 'start': 632.741, 'duration': 6.222}, {'end': 650.048, 'text': 'You can say that something is actually learning rate into, this is a derivative of loss compared to or with respect to W1.', 'start': 640.164, 'duration': 9.884}, {'end': 655.746, 'text': 'learning rate is usually 0.01.', 'start': 652.242, 'duration': 3.504}, {'end': 662.213, 'text': "that's the value people use, but it's a small value, so that you don't adjust your weights too drastically.", 'start': 655.746, 'duration': 6.467}, {'end': 676.897, 'text': 'so learning rate is just limiting your derivative function, and what this is saying is how the loss is changing for a given change in w1 And for W2,', 'start': 662.213, 'duration': 14.684}, {'end': 679.399, 'text': 'the equation is same as W1..', 'start': 676.897, 'duration': 2.502}, {'end': 682.821, 'text': 'And for bias, again the same thing.', 'start': 680.139, 'duration': 2.682}, {'end': 688.426, 'text': "It's just that you're taking a derivative of loss with respect to bias B.", 'start': 683.862, 'duration': 4.564}], 'summary': 'Understanding derivatives is crucial for adjusting weights in machine learning, with learning rate typically set at 0.01 to limit drastic weight adjustments.', 'duration': 70.989, 'max_score': 617.437, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs617437.jpg'}, {'end': 803.706, 'src': 'heatmap', 'start': 719.323, 'weight': 0.969, 'content': [{'end': 724.591, 'text': 'And again, in our derivative video we looked at various ways of calculating derivative function.', 'start': 719.323, 'duration': 5.268}, {'end': 728.556, 'text': 'Mathisfun.com is a great website if you want to refer to math in detail.', 'start': 724.651, 'duration': 3.905}, {'end': 733.842, 'text': 'but for here, just assume that this is the derivative.', 'start': 729.577, 'duration': 4.265}, {'end': 741.029, 'text': 'okay, and again for the law, bias, this is the derivative.', 'start': 733.842, 'duration': 7.187}, {'end': 743.852, 'text': "I have not shown the derivative for w2, but it's same as w1.", 'start': 741.029, 'duration': 2.823}, {'end': 752.809, 'text': 'so now we started with weight one and weight to be one, and our bias was zero.', 'start': 743.852, 'duration': 8.957}, {'end': 765.637, 'text': 'we found a total loss which was 4.31 something, and then we use this derivative to find the new value of weight one and weight two and bias b,', 'start': 752.809, 'duration': 12.828}, {'end': 770, 'text': 'and then we find that the new values will be this.', 'start': 765.637, 'duration': 4.363}, {'end': 773.796, 'text': 'so now my new weight is the 0.8.', 'start': 770, 'duration': 3.796}, {'end': 779.213, 'text': 'W2 is 0.7 and my bias is minus 0.2..', 'start': 773.796, 'duration': 5.417}, {'end': 789.859, 'text': 'Now you repeat, once you got this new weights, you again feed your entire training set to your neural network and you do a forward pass.', 'start': 779.213, 'duration': 10.646}, {'end': 797.523, 'text': 'So you first feed the first sample, you find out the y, hat and y, then you calculate error number one.', 'start': 790.159, 'duration': 7.364}, {'end': 803.706, 'text': 'then you feed the second sample, third sample, until the 13th sample, and then you find the total error.', 'start': 797.523, 'duration': 6.183}], 'summary': 'Calculated derivatives, updated weights: w1=0.8, w2=0.7, bias=-0.2', 'duration': 84.383, 'max_score': 719.323, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs719323.jpg'}, {'end': 854.274, 'src': 'embed', 'start': 807.97, 'weight': 0, 'content': [{'end': 811.451, 'text': 'And by the way, we are using a batch gradient.', 'start': 807.97, 'duration': 3.481}, {'end': 816.892, 'text': 'If you are using stochastic or a mini batch gradient, the technique will be a little different.', 'start': 811.871, 'duration': 5.021}, {'end': 822.634, 'text': 'And we will go into the difference of these three gradient descent techniques in the later videos.', 'start': 817.412, 'duration': 5.222}, {'end': 823.534, 'text': "So don't worry about it.", 'start': 822.654, 'duration': 0.88}, {'end': 835.857, 'text': 'Right now, we are using batch gradient descent where you need to feed entire training data set for one epoch before we start back propagating.', 'start': 824.074, 'duration': 11.783}, {'end': 839.108, 'text': 'So this is the second epoch.', 'start': 837.987, 'duration': 1.121}, {'end': 844.11, 'text': 'And if you remember from our hand-written digits,', 'start': 839.548, 'duration': 4.562}, {'end': 851.973, 'text': 'classification neural network epoch was one of the parameter when you call model.fit in your TensorFlow code.', 'start': 844.11, 'duration': 7.863}, {'end': 854.274, 'text': 'So we have looked at this before.', 'start': 853.034, 'duration': 1.24}], 'summary': 'Using batch gradient descent for second epoch in neural network training.', 'duration': 46.304, 'max_score': 807.97, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs807970.jpg'}], 'start': 617.437, 'title': 'Derivatives in neural networks', 'summary': 'Covers the significance of derivatives in understanding weight and bias adjustments in machine learning, focusing on learning rate of 0.01, log loss derivative in neural networks for weight and bias update, and batch gradient descent in neural networks for training dataset processing.', 'chapters': [{'end': 688.426, 'start': 617.437, 'title': 'Understanding derivatives in machine learning', 'summary': 'Explains the significance of derivatives in understanding how the loss changes for a given change in the weights and bias in machine learning, with a focus on the learning rate of 0.01 to limit drastic weight adjustments.', 'duration': 70.989, 'highlights': ['The derivative of loss with respect to a weight (w1) indicates how the log loss changes for a given change in w1, with a learning rate of 0.01 to limit drastic weight adjustments.', 'The derivative of loss with respect to bias (B) helps understand how the loss changes for a given change in the bias, similar to the understanding for weights (w1 and w2).']}, {'end': 806.728, 'start': 690.481, 'title': 'Neural network log loss derivative', 'summary': 'Discusses the derivative of the log loss function for neural networks, explaining the process of using the derivative to update weights and bias, resulting in new values and subsequent forward pass for error calculation, ultimately concluding the second epoch.', 'duration': 116.247, 'highlights': ['Explaining the process of using the derivative to update weights and bias', 'Process of forward pass for error calculation', 'Reference to mathisfun.com as a resource for detailed math information']}, {'end': 854.274, 'start': 807.97, 'title': 'Batch gradient descent in neural networks', 'summary': 'Explains the concept of batch gradient descent, stating that in batch gradient descent, the entire training dataset is fed for one epoch before backpropagation, and it mentions that the technique will differ for stochastic or mini batch gradient descent.', 'duration': 46.304, 'highlights': ['Batch gradient descent involves feeding the entire training dataset for one epoch before backpropagation.', 'The technique for stochastic or mini batch gradient descent differs from batch gradient descent.', 'Epoch is a parameter in the model.fit function in TensorFlow code.']}], 'duration': 236.837, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs617437.jpg', 'highlights': ['Batch gradient descent involves feeding the entire training dataset for one epoch before backpropagation.', 'The derivative of loss with respect to a weight (w1) indicates how the log loss changes for a given change in w1, with a learning rate of 0.01 to limit drastic weight adjustments.', 'The derivative of loss with respect to bias (B) helps understand how the loss changes for a given change in the bias, similar to the understanding for weights (w1 and w2).', 'The technique for stochastic or mini batch gradient descent differs from batch gradient descent.']}, {'end': 1281.884, 'segs': [{'end': 963.38, 'src': 'embed', 'start': 913.74, 'weight': 1, 'content': [{'end': 921.085, 'text': 'So if your loss function is like a boat, you know, this looks like a boat, the boat in the river, the boat in the river.', 'start': 913.74, 'duration': 7.345}, {'end': 928.249, 'text': 'So you start with some random weight and random B and your loss is usually high.', 'start': 922.225, 'duration': 6.024}, {'end': 929.55, 'text': "So let's say you start at this point.", 'start': 928.289, 'duration': 1.261}, {'end': 939.197, 'text': 'And when you use derivative you are constantly shifting or moving towards a point where your loss is minimal.', 'start': 931.055, 'duration': 8.142}, {'end': 944.178, 'text': 'So here at this bottom point your loss value on the Y axis is minimum.', 'start': 939.897, 'duration': 4.281}, {'end': 954.421, 'text': "All you're trying to do is find out the value of corresponding W1, which is C here maybe 72 or something, and the value of B,", 'start': 945.098, 'duration': 9.323}, {'end': 957.876, 'text': 'which is here near to minus five.', 'start': 954.421, 'duration': 3.455}, {'end': 960.078, 'text': "So you're trying to find these values.", 'start': 958.316, 'duration': 1.762}, {'end': 961.959, 'text': "Once you find these values, you're done.", 'start': 960.538, 'duration': 1.421}, {'end': 963.38, 'text': 'You have your prediction function.', 'start': 962.019, 'duration': 1.361}], 'summary': 'Using derivatives to minimize loss in weight and bias for prediction function.', 'duration': 49.64, 'max_score': 913.74, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs913740.jpg'}, {'end': 1020.173, 'src': 'embed', 'start': 992.902, 'weight': 4, 'content': [{'end': 996.264, 'text': 'And you start at the random point where the loss is very high, which is the star.', 'start': 992.902, 'duration': 3.362}, {'end': 1001.909, 'text': "And you're trying to gradually move to a point where loss is minimal.", 'start': 997.926, 'duration': 3.983}, {'end': 1004.771, 'text': 'And that point is nothing but a global minima.', 'start': 1002.85, 'duration': 1.921}, {'end': 1008.674, 'text': 'So this is your process.', 'start': 1007.673, 'duration': 1.001}, {'end': 1014.212, 'text': 'And in this process, At every point, you are drawing a tangent.', 'start': 1009.295, 'duration': 4.917}, {'end': 1017.532, 'text': 'And that tangent is nothing but a derivative.', 'start': 1015.632, 'duration': 1.9}, {'end': 1020.173, 'text': "We have seen in previous videos that that's a derivative.", 'start': 1017.753, 'duration': 2.42}], 'summary': 'Process involves moving from high loss to global minima by drawing tangents at every point.', 'duration': 27.271, 'max_score': 992.902, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs992902.jpg'}, {'end': 1183.451, 'src': 'embed', 'start': 1104.601, 'weight': 0, 'content': [{'end': 1109.785, 'text': 'You import our train test split, and then you call this function on your data frame.', 'start': 1104.601, 'duration': 5.184}, {'end': 1114.228, 'text': 'My test size is 20% of the samples, okay?', 'start': 1110.826, 'duration': 3.402}, {'end': 1130.529, 'text': 'And if you look at my X train, look something like this okay, and the length, if you look at it, is 22,', 'start': 1115.149, 'duration': 15.38}, {'end': 1134.172, 'text': 'whereas if you look at total number of samples, 28, okay.', 'start': 1130.529, 'duration': 3.643}, {'end': 1139.511, 'text': 'So 22 for training and six for test.', 'start': 1136.789, 'duration': 2.722}, {'end': 1143.813, 'text': "All right, now let's do the scaling.", 'start': 1140.571, 'duration': 3.242}, {'end': 1157.341, 'text': 'So for scaling, you know that if you want to scale your age into zero to one, and since the age is from one to 100, you can just divide it by 100.', 'start': 1143.953, 'duration': 13.388}, {'end': 1158.481, 'text': 'And you can do something like this.', 'start': 1157.341, 'duration': 1.14}, {'end': 1163.243, 'text': 'So here I made a copy of my data frame.', 'start': 1158.702, 'duration': 4.541}, {'end': 1165.384, 'text': "I'm calling it X train scale.", 'start': 1163.663, 'duration': 1.721}, {'end': 1168.585, 'text': "And then I'm dividing age by 100.", 'start': 1166.244, 'duration': 2.341}, {'end': 1174.227, 'text': "OK, same thing I'm doing with the X test.", 'start': 1168.585, 'duration': 5.642}, {'end': 1183.451, 'text': 'So when you do that, my X train scale, you see my age was 22.', 'start': 1175.048, 'duration': 8.403}], 'summary': 'Data split: 20% test size, 22 training samples, scaling age to 0-1.', 'duration': 78.85, 'max_score': 1104.601, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1104601.jpg'}, {'end': 1246.925, 'src': 'embed', 'start': 1215.79, 'weight': 2, 'content': [{'end': 1225.178, 'text': 'okay, so, if you know from my previous tutorial, the way you create keras model is by doing this.', 'start': 1215.79, 'duration': 9.388}, {'end': 1237.073, 'text': "so here i'm creating a sequential model with only one layer, where my neuron is just one and my input shape is two, because age and affordability.", 'start': 1225.178, 'duration': 11.895}, {'end': 1238.455, 'text': 'there are two parameters in the input.', 'start': 1237.073, 'duration': 1.382}, {'end': 1246.925, 'text': 'and my output is one which is telling you whether person will buy insurance or not, and activation is sigmoid.', 'start': 1240.322, 'duration': 6.603}], 'summary': 'Creating a keras sequential model with one neuron and input shape of two for predicting insurance purchase.', 'duration': 31.135, 'max_score': 1215.79, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1215790.jpg'}], 'start': 855.235, 'title': 'Gradient descent and neural network for insurance prediction', 'summary': 'Explains gradient descent for minimizing loss functions and implementing a neural network for insurance prediction using tensorflow, with a 20% test size and age scaling to a range of 0 to 1.', 'chapters': [{'end': 1020.173, 'start': 855.235, 'title': 'Gradient descent for minimizing loss', 'summary': 'Explains the concept of gradient descent in minimizing loss functions, using the analogy of a boat in a river and demonstrating the process of gradually moving towards the global minima to find the optimal weights and bias for the prediction function.', 'duration': 164.938, 'highlights': ['The process of gradient descent involves constantly shifting towards a point where the loss is minimal, represented by the global minima, by drawing tangents at every point, which are derivatives.', 'Describing the loss function as a convex function and using the analogy of a boat in a river to illustrate the gradual movement towards the global minima.', 'Explaining the visualization of the 3D chart with W1 and B on the x-axis and the loss on the y-axis to demonstrate the process of finding the optimal values for W1 and B to obtain the prediction function.']}, {'end': 1281.884, 'start': 1020.853, 'title': 'Neural network for insurance prediction', 'summary': 'Covers the implementation of a neural network for insurance prediction using tensorflow, including data scaling and model building, with a 20% test size and age scaling to a range of 0 to 1.', 'duration': 261.031, 'highlights': ['The chapter demonstrates data preprocessing by performing a 20% train and 80% test split, with 22 samples for training and 6 for testing, showcasing the practical implementation of machine learning techniques.', 'It emphasizes the importance of scaling the data for better model performance, exemplifying the scaling of age to a range of 0 to 1, with the rationale that scaling aids in aligning different features on a similar scale for improved machine learning model performance.', 'The tutorial illustrates the creation of a sequential neural network model using TensorFlow and Keras, with a single layer and one neuron for predicting insurance purchase, providing a fundamental understanding of neural network architecture and its application in insurance prediction.']}], 'duration': 426.649, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs855235.jpg', 'highlights': ['The chapter demonstrates data preprocessing by performing a 20% train and 80% test split, with 22 samples for training and 6 for testing, showcasing the practical implementation of machine learning techniques.', 'Explaining the visualization of the 3D chart with W1 and B on the x-axis and the loss on the y-axis to demonstrate the process of finding the optimal values for W1 and B to obtain the prediction function.', 'The tutorial illustrates the creation of a sequential neural network model using TensorFlow and Keras, with a single layer and one neuron for predicting insurance purchase, providing a fundamental understanding of neural network architecture and its application in insurance prediction.', 'Describing the loss function as a convex function and using the analogy of a boat in a river to illustrate the gradual movement towards the global minima.', 'The process of gradient descent involves constantly shifting towards a point where the loss is minimal, represented by the global minima, by drawing tangents at every point, which are derivatives.', 'It emphasizes the importance of scaling the data for better model performance, exemplifying the scaling of age to a range of 0 to 1, with the rationale that scaling aids in aligning different features on a similar scale for improved machine learning model performance.']}, {'end': 1972.765, 'segs': [{'end': 1345.726, 'src': 'embed', 'start': 1314.391, 'weight': 1, 'content': [{'end': 1316.932, 'text': 'I initially tried 1, 000, 2, 000, whatever.', 'start': 1314.391, 'duration': 2.541}, {'end': 1319.934, 'text': 'And then 5, 000 gave me the best accuracy.', 'start': 1317.773, 'duration': 2.161}, {'end': 1325.996, 'text': 'All right, so you can see the loss was 0.4631.', 'start': 1324.296, 'duration': 1.7}, {'end': 1327.617, 'text': 'So this is that same loss.', 'start': 1325.996, 'duration': 1.621}, {'end': 1338.944, 'text': 'uh, this loss is this one once you go through all your training samples, what was the loss?', 'start': 1330.582, 'duration': 8.362}, {'end': 1345.726, 'text': '4.31. so what happened was when we ran our first epoch see first epoch the loss was 0.7113.', 'start': 1338.944, 'duration': 6.782}], 'summary': 'After testing various values, 5,000 yielded the best accuracy at a loss of 0.4631, which improved from 0.7113 in the first epoch.', 'duration': 31.335, 'max_score': 1314.391, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1314391.jpg'}, {'end': 1410.585, 'src': 'embed', 'start': 1380.195, 'weight': 0, 'content': [{'end': 1385.178, 'text': 'And when you do that, this is your accuracy, see? 1.0.', 'start': 1380.195, 'duration': 4.983}, {'end': 1389.38, 'text': 'So for my taste samples, my model performed excellent.', 'start': 1385.178, 'duration': 4.202}, {'end': 1391.461, 'text': 'It gave you 100% accuracy.', 'start': 1389.94, 'duration': 1.521}, {'end': 1392.922, 'text': "That's what that means.", 'start': 1392.161, 'duration': 0.761}, {'end': 1398.605, 'text': 'And if you want to predict some of the values, so see X taste scale.', 'start': 1394.222, 'duration': 4.383}, {'end': 1402.587, 'text': 'So you have X taste scale and you are predicting the values.', 'start': 1399.265, 'duration': 3.322}, {'end': 1403.247, 'text': "So let's see.", 'start': 1402.647, 'duration': 0.6}, {'end': 1410.585, 'text': 'This is your XT scale and I want to predict.', 'start': 1408.163, 'duration': 2.422}], 'summary': 'The model achieved 100% accuracy in predicting taste samples, demonstrating excellent performance.', 'duration': 30.39, 'max_score': 1380.195, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1380195.jpg'}, {'end': 1473.343, 'src': 'embed', 'start': 1442.608, 'weight': 2, 'content': [{'end': 1444.029, 'text': 'So my model predicted it right.', 'start': 1442.608, 'duration': 1.421}, {'end': 1452.277, 'text': '18 years and 1 affordability will not buy insurance because this is less than 0.5.', 'start': 1444.049, 'duration': 8.228}, {'end': 1452.818, 'text': "So let's see.", 'start': 1452.277, 'duration': 0.541}, {'end': 1459.49, 'text': 'Yeah, see, 18 and one person will not buy the insurance.', 'start': 1455.606, 'duration': 3.884}, {'end': 1464.414, 'text': 'So again, excellent prediction, 61 and one affordability.', 'start': 1459.69, 'duration': 4.724}, {'end': 1467.717, 'text': 'So 61 and one, the person will buy the insurance.', 'start': 1464.895, 'duration': 2.822}, {'end': 1469.299, 'text': "And that's what my model is saying.", 'start': 1467.838, 'duration': 1.461}, {'end': 1473.343, 'text': 'Point 82 is more than point five, which means the person will buy insurance.', 'start': 1469.479, 'duration': 3.864}], 'summary': 'Model accurately predicts insurance purchase based on affordability and age data, achieving 82% accuracy.', 'duration': 30.735, 'max_score': 1442.608, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1442608.jpg'}, {'end': 1720.33, 'src': 'embed', 'start': 1677.888, 'weight': 3, 'content': [{'end': 1687.284, 'text': 'okay, Okay, now let me call this prediction function on this value 0.47 and one.', 'start': 1677.888, 'duration': 9.396}, {'end': 1691.567, 'text': 'So my TensorFlow model give me 0.705.', 'start': 1688.244, 'duration': 3.323}, {'end': 1695.31, 'text': "Let's see if my raw method will give you the same thing.", 'start': 1691.567, 'duration': 3.743}, {'end': 1697.451, 'text': 'Excellent, see 0.7054, 0.7054.', 'start': 1696.691, 'duration': 0.76}, {'end': 1698.112, 'text': 'Got it, cool.', 'start': 1697.451, 'duration': 0.661}, {'end': 1711.983, 'text': 'So what I just showed you was how the prediction function works once you have weights and bias.', 'start': 1705.467, 'duration': 6.516}, {'end': 1720.33, 'text': 'Similarly, I can take the second value, which is 18 and 1.', 'start': 1714.326, 'duration': 6.004}], 'summary': 'Tensorflow model predicts 0.705 for input 0.47 and 1, raw method also gives 0.7054.', 'duration': 42.442, 'max_score': 1677.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1677888.jpg'}, {'end': 1837.556, 'src': 'embed', 'start': 1777.076, 'weight': 4, 'content': [{'end': 1780.457, 'text': 'so sigmoid numpy, which is a vector type of function.', 'start': 1777.076, 'duration': 3.381}, {'end': 1785.9, 'text': 'so instead of taking a one value, if this function takes an array, okay.', 'start': 1780.457, 'duration': 5.443}, {'end': 1792.023, 'text': 'so, for example, if you do 12, 0, 1, it just gives you the sigmoid of the entire array.', 'start': 1785.9, 'duration': 6.123}, {'end': 1802.807, 'text': "and when you're using numpy and vectors, using this, vectors could be very useful in your computation.", 'start': 1792.023, 'duration': 10.784}, {'end': 1805.348, 'text': "so that's why I have this function.", 'start': 1802.807, 'duration': 2.541}, {'end': 1810.369, 'text': "so now I'm going to define a gradient descent function.", 'start': 1805.348, 'duration': 5.021}, {'end': 1815.031, 'text': 'and remember, gradient descent function helps you find out weights.', 'start': 1810.369, 'duration': 4.662}, {'end': 1819.712, 'text': 'so at the end of this function, what if we will find out, is w1, w2 and bias?', 'start': 1815.031, 'duration': 4.681}, {'end': 1831.573, 'text': 'okay? So for age affordability Y, True, Epoch, whatever it will tell you the Okay, let me remove this.', 'start': 1819.712, 'duration': 11.861}, {'end': 1834.834, 'text': "So here I'm specifying how many epoch I want to run.", 'start': 1832.133, 'duration': 2.701}, {'end': 1837.556, 'text': 'I have age, affordability, vitro.', 'start': 1834.854, 'duration': 2.702}], 'summary': 'Using sigmoid numpy for vector computation and defining a gradient descent function to find weights w1, w2, and bias.', 'duration': 60.48, 'max_score': 1777.076, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1777076.jpg'}], 'start': 1282.304, 'title': 'Training and implementing neural networks', 'summary': "Involves training a model with binary cross entropy, adjusting epochs to 5,000 for 91% accuracy, and evaluating the model's 100% accuracy on test data. it also demonstrates the implementation of a neural network in python, achieving an accuracy of 0.7054 and showcasing the prediction function, gradient descent function, and the use of tensorflow keras model.", 'chapters': [{'end': 1469.299, 'start': 1282.304, 'title': 'Model training and evaluation', 'summary': "Involves training a model using binary cross entropy, adjusting epochs to 5,000 for 91% accuracy, and evaluating the model's 100% accuracy on test data, followed by successful predictions based on affordability and age.", 'duration': 186.995, 'highlights': ['The model achieved 100% accuracy on the test data set, indicating excellent performance.', 'The training epoch was adjusted to 5,000, resulting in a 91% accuracy on the training samples.', 'The model correctly predicted insurance purchase decisions based on affordability and age, demonstrating its accuracy in real-world predictions.']}, {'end': 1972.765, 'start': 1469.479, 'title': 'Neural network implementation in python', 'summary': 'Demonstrates the implementation of a neural network in python, showcasing the prediction function, gradient descent function, and the use of tensorflow keras model for weight and bias extraction, achieving an accuracy of 0.7054 and demonstrating the sigmoid and log loss functions.', 'duration': 503.286, 'highlights': ['The prediction function, utilizing weight one (5.0), weight two (1.04), and bias (-2.91) from the TensorFlow Keras model, yields an accuracy of 0.7054 for a specific input, matching the results obtained from the TensorFlow model.', 'The implementation of the gradient descent function in Python involves the initialization of weights (w1 and w2) to 1 and bias to 0, using a learning rate of 0.01, and iterating over epochs to compute the weighted sum and update the weights and bias accordingly.', 'Demonstration of the sigmoid function for calculating the weighted sum and the log loss function for implementing gradient descent, highlighting the vectorized sigmoid numpy method to efficiently process arrays in computations.']}], 'duration': 690.461, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1282304.jpg', 'highlights': ['The model achieved 100% accuracy on the test data set, indicating excellent performance.', 'The training epoch was adjusted to 5,000, resulting in a 91% accuracy on the training samples.', 'The model correctly predicted insurance purchase decisions based on affordability and age, demonstrating its accuracy in real-world predictions.', 'The prediction function, utilizing weight one (5.0), weight two (1.04), and bias (-2.91) from the TensorFlow Keras model, yields an accuracy of 0.7054 for a specific input, matching the results obtained from the TensorFlow model.', 'Demonstration of the sigmoid function for calculating the weighted sum and the log loss function for implementing gradient descent, highlighting the vectorized sigmoid numpy method to efficiently process arrays in computations.', 'The implementation of the gradient descent function in Python involves the initialization of weights (w1 and w2) to 1 and bias to 0, using a learning rate of 0.01, and iterating over epochs to compute the weighted sum and update the weights and bias accordingly.']}, {'end': 2493.155, 'segs': [{'end': 2002.98, 'src': 'embed', 'start': 1974.769, 'weight': 3, 'content': [{'end': 1987.99, 'text': 'OK And what is a y-predicted? Well, y-predicted is nothing but a sigmoid, which is a numpy sigmoid of weighted sum.', 'start': 1974.769, 'duration': 13.221}, {'end': 1991.092, 'text': 'See, friends, until now, it should be very clear.', 'start': 1988.931, 'duration': 2.161}, {'end': 1993.514, 'text': 'These two things are very common for any neuron.', 'start': 1991.132, 'duration': 2.382}, {'end': 1996.656, 'text': 'You take weighted sum and then you apply sigmoid function.', 'start': 1993.694, 'duration': 2.962}, {'end': 2001.199, 'text': 'So if you look at this neuron, you see this vertical line, two part.', 'start': 1997.236, 'duration': 3.963}, {'end': 2002.98, 'text': 'The first part is weighted sum.', 'start': 2001.639, 'duration': 1.341}], 'summary': 'Y-predicted is a sigmoid of weighted sum, a common process for any neuron.', 'duration': 28.211, 'max_score': 1974.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1974769.jpg'}, {'end': 2058.011, 'src': 'embed', 'start': 2026.94, 'weight': 2, 'content': [{'end': 2035.043, 'text': 'Now once we have this, we want to update our weights, okay, based on the derivative functions that we had.', 'start': 2026.94, 'duration': 8.103}, {'end': 2041.085, 'text': 'So what was our derivative function? So in our presentation, the derivative function was this.', 'start': 2035.823, 'duration': 5.262}, {'end': 2046.927, 'text': 'So derivative of loss was xi into y hat minus yi.', 'start': 2041.685, 'duration': 5.242}, {'end': 2053.668, 'text': "So what we're doing is we are subtracting from, so let's do this first.", 'start': 2047.167, 'duration': 6.501}, {'end': 2058.011, 'text': "So let's do y hat minus y i.", 'start': 2054.289, 'duration': 3.722}], 'summary': 'Update weights based on derivative functions: loss = xi(y_hat - yi)', 'duration': 31.071, 'max_score': 2026.94, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs2026940.jpg'}, {'end': 2386.125, 'src': 'embed', 'start': 2305.232, 'weight': 0, 'content': [{'end': 2312.933, 'text': 'Okay And you want to, of course in your function, you want to break when your loss reaches that level.', 'start': 2305.232, 'duration': 7.701}, {'end': 2316.335, 'text': 'I hope it makes sense so far.', 'start': 2315.154, 'duration': 1.181}, {'end': 2327.319, 'text': "So you'll realize that at the end of 366 epoch, I got that loss for 0.4631.", 'start': 2317.235, 'duration': 10.084}, {'end': 2329.46, 'text': 'You know, 0.4631 was the one that I wanted to stop.', 'start': 2327.319, 'duration': 2.141}, {'end': 2337.854, 'text': "Now let's compare that with our cof and intercept.", 'start': 2333.132, 'duration': 4.722}, {'end': 2341.616, 'text': 'So this cof and intercept was the one I got from tensorflow.', 'start': 2338.354, 'duration': 3.262}, {'end': 2348.859, 'text': "So now let's see the tensorflow cof w1 was 5.06.", 'start': 2342.536, 'duration': 6.323}, {'end': 2351, 'text': 'Okay, let me write it down.', 'start': 2348.859, 'duration': 2.141}, {'end': 2367.017, 'text': 'So tensorflow w1 weight was this, w2 was this, And my bias was this.', 'start': 2353.241, 'duration': 13.776}, {'end': 2372.757, 'text': 'And compare it with our plain Python code by the way.', 'start': 2370.495, 'duration': 2.262}, {'end': 2378.761, 'text': 'See 5.05, 5.06, almost same.', 'start': 2373.037, 'duration': 5.724}, {'end': 2381.282, 'text': '1.45, 1.40, again almost same.', 'start': 2379.441, 'duration': 1.841}, {'end': 2386.125, 'text': 'Minus 2.95, minus 2.91, again very very same.', 'start': 2381.322, 'duration': 4.803}], 'summary': 'At the end of 366 epochs, the loss reached 0.4631, and the tensorflow coefficients and intercept closely matched the values from plain python code.', 'duration': 80.893, 'max_score': 2305.232, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs2305232.jpg'}], 'start': 1974.769, 'title': 'Neural network derivative functions and implementing gradient descent in python', 'summary': "Covers the process of calculating y-predicted using a sigmoid function, determining loss with log loss function, and updating weights with the derivative function xi * (y hat - yi) in neural networks. it also outlines the implementation of gradient descent in python to optimize weights and biases, achieving a loss of 0.4200 and replicating tensorflow's results.", 'chapters': [{'end': 2140.122, 'start': 1974.769, 'title': 'Neural network derivative functions', 'summary': 'Explains the process of calculating y-predicted using a sigmoid function of a weighted sum, determining loss using log loss function, and updating weights based on the derivative function xi * (y hat - yi) and its application in neural networks.', 'duration': 165.353, 'highlights': ['The process of calculating y-predicted involves applying a sigmoid function to a weighted sum, which is a common step for any neuron, and then calculating the loss using the log loss function, specifying y true and y predicted.', 'Updating weights involves using the derivative function xi * (y hat - yi) where xi represents a feature such as age or affordability, and then applying the dot product and taking the average to update the weights.', 'The derivative function for updating weights is expressed as xi * (y hat - yi) and involves the multiplication of the difference between y predicted and y true with a feature like age, followed by the dot product and averaging to update the weights.']}, {'end': 2493.155, 'start': 2140.122, 'title': 'Implementing gradient descent in python', 'summary': "Outlines the implementation of gradient descent in python to optimize weights and biases for neural network, achieving a loss of 0.4200 and successfully replicating tensorflow's weights and biases.", 'duration': 353.033, 'highlights': ['Implemented gradient descent to achieve a loss of 0.4200', "Successfully replicated TensorFlow's weights and biases", 'Planned to stop iterations based on loss threshold', 'Prepared for the next tutorial on writing a neural network from scratch']}], 'duration': 518.386, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pXGBHV3y8rs/pics/pXGBHV3y8rs1974769.jpg', 'highlights': ['Implemented gradient descent to achieve a loss of 0.4200', "Successfully replicated TensorFlow's weights and biases", 'Updating weights involves using the derivative function xi * (y hat - yi) where xi represents a feature', 'The process of calculating y-predicted involves applying a sigmoid function to a weighted sum']}], 'highlights': ['The model achieved 100% accuracy on the test data set, indicating excellent performance.', 'The training epoch was adjusted to 5,000, resulting in a 91% accuracy on the training samples.', 'Implemented gradient descent to achieve a loss of 0.4200', 'The prediction function, utilizing weight one (5.0), weight two (1.04), and bias (-2.91) from the TensorFlow Keras model, yields an accuracy of 0.7054 for a specific input, matching the results obtained from the TensorFlow model.', 'The video explains the theory of gradient descent and demonstrates Python code implementation for a simple neural network or logistic regression for an insurance dataset.', 'The tutorial illustrates the creation of a sequential neural network model using TensorFlow and Keras, with a single layer and one neuron for predicting insurance purchase, providing a fundamental understanding of neural network architecture and its application in insurance prediction.', 'Understanding the math and working of gradient descent is vital for roles in the ML domain, as it forms the core of machine learning, especially in supervised machine learning.', 'Gradient descent is crucial in supervised machine learning, especially in statistical and deep learning, and is essential for data scientist and machine learning engineer roles.', 'The process of calculating y-predicted involves applying a sigmoid function to a weighted sum', 'The derivative of loss with respect to a weight (w1) indicates how the log loss changes for a given change in w1, with a learning rate of 0.01 to limit drastic weight adjustments.']}