title
TensorFlow 2.0 Crash Course

description
Learn how to use TensorFlow 2.0 in this crash course for beginners. This course will demonstrate how to create neural networks with Python and TensorFlow 2.0. If you want a more comprehensive TensorFlow 2.0 course, check out this 7 hour course: https://youtu.be/tPYj3fFJGjk 🎥 Course created by Tech with Tim. Check out his YouTube channel: https://www.youtube.com/channel/UC4JX40jDee_tINbkjycV4Sg ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) What is a Neural Network? ⌨️ (0:26:34) How to load & look at data ⌨️ (0:39:38) How to create a model ⌨️ (0:56:48) How to use the model to make predictions ⌨️ (1:07:11) Text Classification (part 1) ⌨️ (1:28:37) What is an Embedding Layer? Text Classification (part 2) ⌨️ (1:42:30) How to train the model - Text Classification (part 3) ⌨️ (1:52:35) How to saving & loading models - Text Classification (part 4) ⌨️ (2:07:09) How to install TensorFlow GPU on Linux -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://www.freecodecamp.org/news

detail
{'title': 'TensorFlow 2.0 Crash Course', 'heatmap': [{'end': 1680.597, 'start': 1599.554, 'weight': 1}], 'summary': "Tutorial series 'tensorflow 2.0 crash course' covers neural network fundamentals, training, activation functions, model predictions, text and data processing, nlp, model saving/loading, achieving 91% test accuracy after 7 epochs, and installing tensorflow 2.0 gpu version on ubuntu.", 'chapters': [{'end': 145.221, 'segs': [{'end': 89.586, 'src': 'embed', 'start': 40.563, 'weight': 0, 'content': [{'end': 46.105, 'text': 'Now, the beginning videos, and especially this one are going to be dedicated to understanding how a neural network works.', 'start': 40.563, 'duration': 5.542}, {'end': 56.711, 'text': "And I think this is absolutely fundamental and that you have to have some kind of basis on the math behind a neural network before you're really able to actually properly implement one.", 'start': 46.445, 'duration': 10.266}, {'end': 62.855, 'text': 'Now, TensorFlow does a really nice job of making it super easy to implement neural networks and to use them,', 'start': 57.252, 'duration': 5.603}, {'end': 68.739, 'text': 'but to actually have a successful and complex neural network you have to understand how they work on the lower level.', 'start': 62.855, 'duration': 5.884}, {'end': 70.86, 'text': "So that's what we're going to be doing for the first few videos.", 'start': 68.819, 'duration': 2.041}, {'end': 71.96, 'text': 'After that,', 'start': 71.42, 'duration': 0.54}, {'end': 79.443, 'text': "what we'll do is we'll start designing our own neural networks that can solve the very basic MNIST data sets that TensorFlow provides to us.", 'start': 71.96, 'duration': 7.483}, {'end': 82.283, 'text': 'Now these are pretty straightforward and pretty simple,', 'start': 80.243, 'duration': 2.04}, {'end': 87.125, 'text': 'but they give us a really good building block on understanding how the architecture of a neural network works.', 'start': 82.283, 'duration': 4.842}, {'end': 89.586, 'text': 'What are some of the different activation functions,', 'start': 87.445, 'duration': 2.141}], 'summary': "The initial videos focus on understanding neural network fundamentals and tensorflow's role in implementation, before moving on to designing networks for solving basic datasets.", 'duration': 49.023, 'max_score': 40.563, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH30440563.jpg'}], 'start': 0.489, 'title': 'Neural networks with tensorflow 2.0', 'summary': 'Introduces a tutorial series on neural networks with python and tensorflow 2.0, covering the basics of neural network implementation, understanding the architecture, and designing a neural network to play a basic game using tensorflow and python.', 'chapters': [{'end': 145.221, 'start': 0.489, 'title': 'Neural networks with tensorflow 2.0', 'summary': 'Introduces a tutorial series on neural networks with python and tensorflow 2.0, covering the basics of neural network implementation, understanding the architecture, and designing a neural network to play a basic game using tensorflow and python.', 'duration': 144.732, 'highlights': ['The tutorial series provides an overview of the basics of neural network implementation, understanding the architecture, and designing a neural network to play a basic game using TensorFlow and Python. This tutorial series covers the fundamentals of neural network implementation, understanding the architecture, and designing a neural network to play a basic game using TensorFlow and Python.', 'The series starts with dedicated videos to understand the math behind a neural network, essential for successful and complex neural network implementation. The beginning of the series focuses on understanding the math behind a neural network, essential for successful and complex neural network implementation.', 'The series progresses to designing neural networks to solve the MNIST datasets, providing a solid understanding of neural network architecture and activation functions. Later, it progresses to designing neural networks to solve the MNIST datasets, providing a solid understanding of neural network architecture and activation functions.']}], 'duration': 144.732, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH304489.jpg', 'highlights': ['The tutorial series covers the fundamentals of neural network implementation, understanding the architecture, and designing a neural network to play a basic game using TensorFlow and Python.', 'The beginning of the series focuses on understanding the math behind a neural network, essential for successful and complex neural network implementation.', 'Later, it progresses to designing neural networks to solve the MNIST datasets, providing a solid understanding of neural network architecture and activation functions.']}, {'end': 830.11, 'segs': [{'end': 269.318, 'src': 'embed', 'start': 240.788, 'weight': 3, 'content': [{'end': 243.632, 'text': "And then it goes, it's just a chain of firing and unfiring.", 'start': 240.788, 'duration': 2.844}, {'end': 248.618, 'text': "And that's just kind of how it works, right? Firing and unfiring.", 'start': 244.613, 'duration': 4.005}, {'end': 254.366, 'text': "Now that's, as far as I'm going to go into explaining neurons, but this kind of gives us a little bit of a basis for a neural network.", 'start': 249.38, 'duration': 4.986}, {'end': 262.313, 'text': 'Now a neural network essentially is a connected layer of neurons or connected layers, so multiple of neurons.', 'start': 254.887, 'duration': 7.426}, {'end': 269.318, 'text': "So, in this case, let's say that we have a first layer we're going to call this our input layer that has four neurons,", 'start': 262.833, 'duration': 6.485}], 'summary': 'Neural network consists of connected layers of neurons, with the input layer having four neurons.', 'duration': 28.53, 'max_score': 240.788, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH304240788.jpg'}, {'end': 438.878, 'src': 'embed', 'start': 393.973, 'weight': 0, 'content': [{'end': 395.395, 'text': 'input results in an output.', 'start': 393.973, 'duration': 1.422}, {'end': 399.26, 'text': 'In this case, we have four inputs, and we have one output.', 'start': 395.816, 'duration': 3.444}, {'end': 407.164, 'text': "but we could have a case where we have four inputs, and we have 25 outputs, right, it really depends on the kind of problem we're trying to solve.", 'start': 399.781, 'duration': 7.383}, {'end': 409.365, 'text': 'So this is a very simple example.', 'start': 407.884, 'duration': 1.481}, {'end': 416.548, 'text': "But what I'm going to do is show you how we would or how a neural network would work to train a very basic snake game.", 'start': 409.425, 'duration': 7.123}, {'end': 420.29, 'text': "So let's look at a very basic snake game.", 'start': 417.348, 'duration': 2.942}, {'end': 422.132, 'text': "So let's say this is our snake.", 'start': 420.35, 'duration': 1.782}, {'end': 424.573, 'text': 'Okay And this is his head.', 'start': 422.272, 'duration': 2.301}, {'end': 428.256, 'text': "Actually, yeah, let's say this is his head.", 'start': 426.375, 'duration': 1.881}, {'end': 431.358, 'text': 'But like this is what the position the snake looks like where this is the tail.', 'start': 428.296, 'duration': 3.062}, {'end': 432.419, 'text': "Okay, we'll circle the tail.", 'start': 431.418, 'duration': 1.001}, {'end': 438.878, 'text': 'Now, what I want to do is I want to train a neural network that will allow this snake to stay alive.', 'start': 433.772, 'duration': 5.106}], 'summary': 'A demonstration of training a neural network for a basic snake game with one input and one output.', 'duration': 44.905, 'max_score': 393.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH304393973.jpg'}, {'end': 633.098, 'src': 'embed', 'start': 606.16, 'weight': 2, 'content': [{'end': 609.503, 'text': 'So we have a little bit more room to work with some math stuff right here.', 'start': 606.16, 'duration': 3.343}, {'end': 615.409, 'text': "But right now, what we start by doing is we start by designing what's known as the architecture of our neural network.", 'start': 610.103, 'duration': 5.306}, {'end': 618.392, 'text': "So we've already done this, we have the input and we have the output.", 'start': 615.429, 'duration': 2.963}, {'end': 621.673, 'text': 'Now, each of our inputs is connected to our outputs.', 'start': 619.172, 'duration': 2.501}, {'end': 624.595, 'text': "And each of these connections has what's known as a weight.", 'start': 621.933, 'duration': 2.662}, {'end': 633.098, 'text': 'Now, another thing that we have is each of our input neurons has a value, right? We had in this case, we either had zero, or we had one.', 'start': 625.315, 'duration': 7.783}], 'summary': 'Designing the architecture of a neural network with input and output connections, and weights for each connection.', 'duration': 26.938, 'max_score': 606.16, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH304606160.jpg'}], 'start': 145.501, 'title': 'Neural network fundamentals', 'summary': 'Covers the basic principles of neural networks, including neuron function and network composition, and emphasizes training a snake game with inputs and outputs. it also discusses network architecture, training process, and achieving valid outputs using a large dataset.', 'chapters': [{'end': 588.732, 'start': 145.501, 'title': 'Understanding neural networks', 'summary': 'Explains the basic principles of neural networks, detailing the function of neurons, the composition of neural networks, and their application in training a snake game, with an emphasis on determining inputs and outputs for network training.', 'duration': 443.231, 'highlights': ['Neurons and their connections form the basis of a neural network, with the firing and unfiring of neurons creating a chain reaction within the network. Neurons in a network can either fire or not fire, leading to a chain reaction of firing and unfiring among connected neurons.', 'Neural networks consist of connected layers of neurons, with connections and weights determined based on the type of network being used. Neural networks are composed of connected layers of neurons, with the connections and weights determined by the specific type of network being utilized.', "The process of training a neural network is exemplified through the application of a basic snake game, where inputs and outputs are determined to enable the snake to stay alive. The training of a neural network is illustrated through the example of training a snake game, where inputs and outputs are defined to ensure the snake's survival."]}, {'end': 830.11, 'start': 589.073, 'title': 'Neural network architecture and training', 'summary': 'Discusses the design and training of a neural network, covering the architecture with inputs, weights, and biases, as well as the process of training the network with a large dataset to achieve valid outputs.', 'duration': 241.037, 'highlights': ["The network architecture involves connecting input and output neurons, each with associated weights and biases, allowing for the calculation of a weighted sum to produce the output value, which forms the basis of the network's functionality and performance.", 'Training the network involves using a large dataset, such as playing 1000 games of snake, to gather diverse inputs and outputs, enabling the network to learn and generate valid outputs based on the trained information.', 'The concept of weighted sums, incorporating values, weights, and biases, forms the fundamental process for determining the output value of the neural network, essential for understanding its functionality and training procedures.']}], 'duration': 684.609, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH304145501.jpg', 'highlights': ['The process of training a neural network is exemplified through the application of a basic snake game, where inputs and outputs are determined to enable the snake to stay alive.', 'Training the network involves using a large dataset, such as playing 1000 games of snake, to gather diverse inputs and outputs, enabling the network to learn and generate valid outputs based on the trained information.', "The network architecture involves connecting input and output neurons, each with associated weights and biases, allowing for the calculation of a weighted sum to produce the output value, which forms the basis of the network's functionality and performance.", 'Neurons and their connections form the basis of a neural network, with the firing and unfiring of neurons creating a chain reaction within the network.']}, {'end': 1723.134, 'segs': [{'end': 943.521, 'src': 'embed', 'start': 907.183, 'weight': 0, 'content': [{'end': 913.066, 'text': "And what it will do is it'll start adjusting these weights in these biases, so that it gets more things correct.", 'start': 907.183, 'duration': 5.883}, {'end': 918.287, 'text': "So obviously, that's why neural networks typically take a massive amount of information to train.", 'start': 913.466, 'duration': 4.821}, {'end': 923.828, 'text': 'Because what you do is you pass it all of this information, and then it keeps going through the network.', 'start': 918.647, 'duration': 5.181}, {'end': 929.97, 'text': 'And at the beginning, it sucks, right? Because it has this network just starts with random weights and random biases.', 'start': 923.909, 'duration': 6.061}, {'end': 935.712, 'text': 'But as it goes through, and it learns it says, Okay, well, I got this one correct.', 'start': 930.35, 'duration': 5.362}, {'end': 938.175, 'text': "So let's leave the weights and the biases the same.", 'start': 935.993, 'duration': 2.182}, {'end': 941.519, 'text': "But let's remember that this is what the way in the bias was when this was correct.", 'start': 938.195, 'duration': 3.324}, {'end': 943.521, 'text': 'And then maybe you get something wrong.', 'start': 942.019, 'duration': 1.502}], 'summary': 'Neural networks require massive data to adjust weights and biases for accuracy.', 'duration': 36.338, 'max_score': 907.183, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH304907183.jpg'}, {'end': 1019.143, 'src': 'embed', 'start': 992.324, 'weight': 1, 'content': [{'end': 999.349, 'text': 'So, as long as you understand that when you feed information, what happens is it checks whether the network got it correct or got it incorrect,', 'start': 992.324, 'duration': 7.025}, {'end': 1001.09, 'text': 'and then it adjusts the network accordingly.', 'start': 999.349, 'duration': 1.741}, {'end': 1004.293, 'text': 'And that is how the learning process works for a neural network.', 'start': 1001.351, 'duration': 2.942}, {'end': 1009.136, 'text': "Alright, so now it's time to discuss a little bit about activation functions.", 'start': 1004.753, 'duration': 4.383}, {'end': 1015.16, 'text': "So right now, what I've actually just described to you is a very advanced technique of linear regression.", 'start': 1009.556, 'duration': 5.604}, {'end': 1019.143, 'text': "So essentially, I was saying we are adjusting weights, we're adjusting biases.", 'start': 1015.761, 'duration': 3.382}], 'summary': 'Neural network learning process involves adjusting weights and biases based on correct and incorrect information, akin to advanced linear regression.', 'duration': 26.819, 'max_score': 992.324, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH304992324.jpg'}, {'end': 1118.67, 'src': 'embed', 'start': 1090.239, 'weight': 3, 'content': [{'end': 1092.382, 'text': 'And when an activation function does, is,', 'start': 1090.239, 'duration': 2.143}, {'end': 1102.252, 'text': "it's essentially a nonlinear function that will allow you to add a degree of complexity to your network so that you can have more of a function that's like this.", 'start': 1092.382, 'duration': 9.87}, {'end': 1105.196, 'text': 'as opposed to a function that is a straight line.', 'start': 1102.913, 'duration': 2.283}, {'end': 1110.061, 'text': 'So an example of an activation function is something like a sigmoid function.', 'start': 1105.696, 'duration': 4.365}, {'end': 1118.67, 'text': "Now a sigmoid function, what it does is it'll map any value you give it in between the value of negative one and one.", 'start': 1110.621, 'duration': 8.049}], 'summary': 'Activation functions add nonlinearity to neural networks, e.g. sigmoid maps values between -1 and 1.', 'duration': 28.431, 'max_score': 1090.239, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3041090239.jpg'}, {'end': 1396.442, 'src': 'embed', 'start': 1368.589, 'weight': 4, 'content': [{'end': 1371.03, 'text': "Well, we use what's known as a loss function.", 'start': 1368.589, 'duration': 2.441}, {'end': 1375.251, 'text': 'So a loss function, essentially is a way of calculating error.', 'start': 1371.79, 'duration': 3.461}, {'end': 1377.931, 'text': "Now there's a ton of different loss loss functions.", 'start': 1375.751, 'duration': 2.18}, {'end': 1380.192, 'text': 'some of them are like mean squared error.', 'start': 1377.931, 'duration': 2.261}, {'end': 1381.392, 'text': "that's the name of one of them.", 'start': 1380.192, 'duration': 1.2}, {'end': 1385.614, 'text': "I think one is like I can't even remember the name of this one.", 'start': 1381.392, 'duration': 4.222}, {'end': 1387.215, 'text': "But there's there's a bunch of very popular ones.", 'start': 1385.654, 'duration': 1.561}, {'end': 1388.776, 'text': 'If you know some, leave them in the comments.', 'start': 1387.315, 'duration': 1.461}, {'end': 1390.117, 'text': 'Love to hear all the different ones.', 'start': 1388.916, 'duration': 1.201}, {'end': 1396.442, 'text': 'But anyways, what the loss function will do is tell you how wrong your answer is.', 'start': 1390.858, 'duration': 5.584}], 'summary': 'Loss functions calculate error, e.g. mean squared error, to measure answer accuracy.', 'duration': 27.853, 'max_score': 1368.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3041368589.jpg'}, {'end': 1680.597, 'src': 'heatmap', 'start': 1599.554, 'weight': 1, 'content': [{'end': 1600.454, 'text': "Now, in today's video,", 'start': 1599.554, 'duration': 0.9}, {'end': 1606.016, 'text': "what we're going to be doing is actually getting our hands dirty and working with a bit of code and loading in our first data set.", 'start': 1600.454, 'duration': 5.562}, {'end': 1608.537, 'text': "So we're not actually going to do anything with the model right now.", 'start': 1606.296, 'duration': 2.241}, {'end': 1609.917, 'text': "We're going to do that in the next video.", 'start': 1608.557, 'duration': 1.36}, {'end': 1615.259, 'text': 'This video is going to be dedicated to understanding data, the importance of data, how we can scale that data,', 'start': 1609.997, 'duration': 5.262}, {'end': 1619.32, 'text': "look at it and understand how that's going to affect our model when training.", 'start': 1615.259, 'duration': 4.061}, {'end': 1623.662, 'text': 'The most important part of machine learning, at least in my opinion is the data.', 'start': 1620.12, 'duration': 3.542}, {'end': 1631.526, 'text': "And it's also one of the hardest things to actually get done correctly, training the model and testing the model and using it as actually very easy.", 'start': 1623.942, 'duration': 7.584}, {'end': 1633.387, 'text': 'And you guys will see that as we go through.', 'start': 1631.546, 'duration': 1.841}, {'end': 1643.092, 'text': "but getting the right information to our model and having it in the correct form is something that is way more challenging than it may seem with these initial data sets that we're going to work with.", 'start': 1633.387, 'duration': 9.705}, {'end': 1646.354, 'text': 'Things are going to be very easy because the data sets are going to be given to us.', 'start': 1643.132, 'duration': 3.222}, {'end': 1651.157, 'text': "but when we move on into future videos to using our own data, we're going to have to pre process it.", 'start': 1646.354, 'duration': 4.803}, {'end': 1654.621, 'text': "we're gonna have to put it in its correct form, we're going to have to send it into an array.", 'start': 1651.157, 'duration': 3.464}, {'end': 1656.643, 'text': "we're going to have to make sure that the data makes sense.", 'start': 1654.621, 'duration': 2.022}, {'end': 1660.928, 'text': "So we're not adding things that shouldn't be there, or we're not omitting things that need to be there.", 'start': 1656.683, 'duration': 4.245}, {'end': 1669.422, 'text': "So anyways, I'm just going to quickly say here that I am kind of working off of this TensorFlow 2.0 tutorial that is on TensorFlow's website.", 'start': 1661.368, 'duration': 8.054}, {'end': 1672.646, 'text': "Now I'm kind of going to stray from it quite a bit, to be honest,", 'start': 1669.882, 'duration': 2.764}, {'end': 1677.152, 'text': "but I'm just using the data sets that they have and a little bit of the code that they have here,", 'start': 1672.646, 'duration': 4.506}, {'end': 1680.597, 'text': "because it's a very nice introduction to machine learning and neural networks.", 'start': 1677.152, 'duration': 3.445}], 'summary': 'Importance of understanding and processing data for machine learning, using tensorflow 2.0 tutorial.', 'duration': 81.043, 'max_score': 1599.554, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3041599554.jpg'}, {'end': 1643.092, 'src': 'embed', 'start': 1609.997, 'weight': 5, 'content': [{'end': 1615.259, 'text': 'This video is going to be dedicated to understanding data, the importance of data, how we can scale that data,', 'start': 1609.997, 'duration': 5.262}, {'end': 1619.32, 'text': "look at it and understand how that's going to affect our model when training.", 'start': 1615.259, 'duration': 4.061}, {'end': 1623.662, 'text': 'The most important part of machine learning, at least in my opinion is the data.', 'start': 1620.12, 'duration': 3.542}, {'end': 1631.526, 'text': "And it's also one of the hardest things to actually get done correctly, training the model and testing the model and using it as actually very easy.", 'start': 1623.942, 'duration': 7.584}, {'end': 1633.387, 'text': 'And you guys will see that as we go through.', 'start': 1631.546, 'duration': 1.841}, {'end': 1643.092, 'text': "but getting the right information to our model and having it in the correct form is something that is way more challenging than it may seem with these initial data sets that we're going to work with.", 'start': 1633.387, 'duration': 9.705}], 'summary': "Understanding data's importance in machine learning, and its challenge in obtaining and preparing correctly for model training.", 'duration': 33.095, 'max_score': 1609.997, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3041609997.jpg'}], 'start': 830.21, 'title': 'Neural network training and activation functions', 'summary': 'Explains the training process of a neural network, emphasizing the need for a massive amount of data. it also covers activation functions like sigmoid and rectified linear unit, and the role of data preprocessing in machine learning.', 'chapters': [{'end': 1009.136, 'start': 830.21, 'title': 'Neural network training', 'summary': 'Explains the training process of a neural network, where the network adjusts the weights and biases based on input data to improve accuracy, typically requiring a massive amount of information to train effectively.', 'duration': 178.926, 'highlights': ['Neural network training involves adjusting weights and biases based on input data to improve accuracy, typically requiring a massive amount of information to train effectively. adjusting weights and biases, improving accuracy, massive amount of information', 'The network adjusts biases and weights based on input data, correcting its responses when they are incorrect, and eventually aims for high accuracy or a correct answer. adjusting biases and weights, correcting responses, aiming for high accuracy', "The learning process for a neural network involves checking whether the network's response to input data is correct or incorrect, and then adjusting the network accordingly. learning process, checking responses, adjusting the network"]}, {'end': 1723.134, 'start': 1009.556, 'title': 'Neural networks & activation functions', 'summary': 'Explains the advanced technique of linear regression in neural networks, the importance of activation functions in adding complexity to the model, including examples of sigmoid and rectified linear unit functions, and the role of loss functions in calculating error and adjusting weights and biases. it also emphasizes the significance of data preprocessing in machine learning.', 'duration': 713.578, 'highlights': ['Importance of Activation Functions The activation functions add complexity to the network, allowing for more complex functions to be created, such as a sigmoid function that maps values between -1 and 1, and a rectified linear unit function that enhances the complexity of the model.', "Role of Loss Functions Loss functions calculate error, providing a better understanding of the model's accuracy, and aid in adjusting weights and biases based on the degree of error, thus adding complexity to the model.", "Data Preprocessing in Machine Learning Emphasizes the importance of correctly pre-processing and formatting data for the model, as it significantly impacts the model's training and testing, highlighting the complexity of preparing data for neural networks."]}], 'duration': 892.924, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH304830210.jpg', 'highlights': ['Neural network training involves adjusting weights and biases based on input data to improve accuracy, typically requiring a massive amount of information to train effectively.', "The learning process for a neural network involves checking whether the network's response to input data is correct or incorrect, and then adjusting the network accordingly.", 'The network adjusts biases and weights based on input data, correcting its responses when they are incorrect, and eventually aims for high accuracy or a correct answer.', 'Importance of Activation Functions The activation functions add complexity to the network, allowing for more complex functions to be created, such as a sigmoid function that maps values between -1 and 1, and a rectified linear unit function that enhances the complexity of the model.', "Role of Loss Functions Loss functions calculate error, providing a better understanding of the model's accuracy, and aid in adjusting weights and biases based on the degree of error, thus adding complexity to the model.", "Data Preprocessing in Machine Learning Emphasizes the importance of correctly pre-processing and formatting data for the model, as it significantly impacts the model's training and testing, highlighting the complexity of preparing data for neural networks."]}, {'end': 3468.141, 'segs': [{'end': 1826.181, 'src': 'embed', 'start': 1795.797, 'weight': 1, 'content': [{'end': 1799.879, 'text': "Now, another thing we're going to install here is going to be matplotlib.", 'start': 1795.797, 'duration': 4.082}, {'end': 1806.743, 'text': "Now matplotlib is a nice library for just graphing and showing images and different information that we'll use a lot through this series.", 'start': 1800.34, 'duration': 6.403}, {'end': 1810.105, 'text': "So let's install that I already have it installed, but go ahead and do that.", 'start': 1807.123, 'duration': 2.982}, {'end': 1812.707, 'text': 'And then finally, we will install pandas.', 'start': 1810.605, 'duration': 2.102}, {'end': 1816.691, 'text': 'which we may be using in later videos in the series.', 'start': 1813.527, 'duration': 3.164}, {'end': 1818.032, 'text': 'So I figured we might as well install it now.', 'start': 1816.711, 'duration': 1.321}, {'end': 1819.494, 'text': 'So pip install pandas.', 'start': 1818.492, 'duration': 1.002}, {'end': 1826.181, 'text': "And once you've done that, you should be ready to actually go here and start getting our data loaded in and looking at the data.", 'start': 1819.594, 'duration': 6.587}], 'summary': 'Install matplotlib and pandas for data visualization and analysis.', 'duration': 30.384, 'max_score': 1795.797, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3041795797.jpg'}, {'end': 2163.361, 'src': 'embed', 'start': 2140.772, 'weight': 2, 'content': [{'end': 2148.284, 'text': 'So now I want to show you what some of these images look like and talk about the architecture of the neural network we might use in the next video.', 'start': 2140.772, 'duration': 7.512}, {'end': 2155.219, 'text': "So I'm going to use pi plot just to show you some of these images and explain kind of the input and the output and all of that.", 'start': 2149.318, 'duration': 5.901}, {'end': 2161.021, 'text': 'So if if you want to show an image using matplotlib, you can do this by just doing PLT dot I am show.', 'start': 2155.679, 'duration': 5.342}, {'end': 2163.361, 'text': 'And then in here simply putting the image.', 'start': 2161.961, 'duration': 1.4}], 'summary': 'Exploring image visualization and neural network architecture using matplotlib and pi plot.', 'duration': 22.589, 'max_score': 2140.772, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3042140772.jpg'}, {'end': 2560.096, 'src': 'embed', 'start': 2538.347, 'weight': 3, 'content': [{'end': 2547.251, 'text': 'So when we flatten that data, so 28 rows of 28 pixels, then we end up getting 784 pixels, just one after each other.', 'start': 2538.347, 'duration': 8.904}, {'end': 2549.893, 'text': "And that's what we're going to feed in as the input to our neural network.", 'start': 2547.312, 'duration': 2.581}, {'end': 2557.715, 'text': "So that means that our initial input layer is going to look something like this we're going to have a bunch of neurons and they're going to go all the way down.", 'start': 2550.513, 'duration': 7.202}, {'end': 2560.096, 'text': "So we're going to have 784 neurons.", 'start': 2557.835, 'duration': 2.261}], 'summary': 'Flattened data from 28x28 pixels results in 784 pixels, serving as input for 784 neurons in the initial layer of the neural network.', 'duration': 21.749, 'max_score': 2538.347, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3042538347.jpg'}, {'end': 2987.193, 'src': 'embed', 'start': 2953.35, 'weight': 4, 'content': [{'end': 2955.05, 'text': "sorry, and we're going to have 10 neurons.", 'start': 2953.35, 'duration': 1.7}, {'end': 2957.131, 'text': 'And this is going to be our output layer.', 'start': 2955.13, 'duration': 2.001}, {'end': 2960.372, 'text': "And we're going to have an activation of softmax.", 'start': 2957.151, 'duration': 3.221}, {'end': 2967.879, 'text': 'Now, what softmax does is exactly what I explained when showing you that kind of architecture picture.', 'start': 2961.471, 'duration': 6.408}, {'end': 2973.205, 'text': 'it will pick values for each neuron so that all of those values add up to one.', 'start': 2967.879, 'duration': 5.326}, {'end': 2979.051, 'text': "So essentially, it is like the probability of the network thinking it's a certain value.", 'start': 2973.486, 'duration': 5.565}, {'end': 2987.193, 'text': "So it's like, I believe that it's 80%, this 2%, this 5%, this, but all of the neurons there, those values will add up to one.", 'start': 2979.071, 'duration': 8.122}], 'summary': 'The output layer will have 10 neurons with softmax activation, assigning probabilities to each value with the sum equalling one.', 'duration': 33.843, 'max_score': 2953.35, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3042953350.jpg'}, {'end': 3400.032, 'src': 'embed', 'start': 3372.87, 'weight': 0, 'content': [{'end': 3375.912, 'text': 'And obviously, you can see the tweaked accuracy as we continue to go.', 'start': 3372.87, 'duration': 3.042}, {'end': 3380.295, 'text': "I'm interested to see here if we're going to increase by much or if it just kind of going to stay at the same level.", 'start': 3376.293, 'duration': 4.002}, {'end': 3384.882, 'text': "Alright, so we're hitting about 90%.", 'start': 3382.64, 'duration': 2.242}, {'end': 3386.263, 'text': "And let's see here.", 'start': 3384.882, 'duration': 1.381}, {'end': 3390.305, 'text': '91 Okay, so we got up to 91%.', 'start': 3386.283, 'duration': 4.022}, {'end': 3394.869, 'text': 'But you can see that it was kind of diminishing returns as soon as we ended up getting to about seven epochs.', 'start': 3390.305, 'duration': 4.564}, {'end': 3397.43, 'text': 'Even Yeah, even like eight epochs.', 'start': 3395.369, 'duration': 2.061}, {'end': 3400.032, 'text': 'After this, we only increased by marginal amount.', 'start': 3397.811, 'duration': 2.221}], 'summary': 'Model accuracy reached 91% after 7 epochs, with diminishing returns thereafter.', 'duration': 27.162, 'max_score': 3372.87, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3043372870.jpg'}], 'start': 1723.194, 'title': 'Tensorflow installation, data visualization, architecture, training, and accuracy', 'summary': 'Discusses installing tensorflow 2.0, addressing potential numpy issues, installing additional libraries like matplotlib and pandas, loading and splitting the dataset using keras. it also covers data visualization of images using matplotlib, preprocessing data, and discussing neural network architecture for image classification. additionally, the process of flattening data for neural network input, designing a neural network architecture, and training the model is explained, resulting in a 91% test accuracy after training with 7 epochs.', 'chapters': [{'end': 2140.492, 'start': 1723.194, 'title': 'Installing tensorflow and required libraries', 'summary': 'Discusses the installation of tensorflow 2.0, including potential issues with numpy, and the installation of additional libraries like matplotlib and pandas. it also covers loading and splitting the dataset using keras, as well as understanding the labels for the dataset.', 'duration': 417.298, 'highlights': ['The chapter discusses the installation of TensorFlow 2.0, including potential issues with NumPy, and the installation of additional libraries like matplotlib and pandas.', 'It covers loading and splitting the dataset using Keras, as well as understanding the labels for the dataset.']}, {'end': 2477.917, 'start': 2140.772, 'title': 'Neural network architecture & data visualization', 'summary': 'Covers data visualization of images using matplotlib, understanding the 28 by 28 pixel array format, preprocessing the data by scaling pixel values, and discussing the architecture of the neural network for image classification.', 'duration': 337.145, 'highlights': ['The chapter covers data visualization of images using matplotlib The speaker demonstrates using matplotlib to visualize and explain the input and output of images, providing practical examples.', 'Understanding the 28 by 28 pixel array format The speaker explains that the images are arrays of 28 by 28 pixels, showcasing a specific image and displaying the pixel values as representative of grayscale values.', 'Preprocessing the data by scaling pixel values The speaker discusses the need to scale down the pixel values from 0-255 to 0-1 for easier processing by dividing all pixel values by 255.', 'Discussing the architecture of the neural network for image classification The speaker explains the need to transform the input data into a format suitable for the neural network and emphasizes the importance of understanding the layers and their roles in the network.']}, {'end': 3008.999, 'start': 2478.597, 'title': 'Data flattening and neural network architecture', 'summary': 'Explains the process of flattening data for neural network input, using a 28x28 pixel example to generate 784 pixels, and describes the design of a neural network architecture with a flattened input layer, a hidden layer of 128 neurons, and an output layer of 10 neurons, each representing a different class, with an activation function of softmax.', 'duration': 530.402, 'highlights': ['The process of flattening data for neural network input is explained using a 28x28 pixel example to generate 784 pixels, enabling the configuration of a flattened input layer for the neural network. Flattening 28x28 pixel data results in 784 pixels for the neural network input, facilitating the creation of a flattened input layer.', 'The design of the neural network architecture is detailed, featuring a flattened input layer, a hidden layer of 128 neurons, and an output layer of 10 neurons, each representing a different class. The neural network architecture comprises a flattened input layer, a hidden layer with 128 neurons, and an output layer with 10 neurons, each representing a distinct class.', "The activation function of softmax is described, indicating its role in assigning values to each neuron so that their sum equals one, allowing the determination of the network's probability for each class. The softmax activation function assigns values to neurons so that their sum equals one, providing the network's probabilities for each class."]}, {'end': 3468.141, 'start': 3009.019, 'title': 'Neural network model training', 'summary': 'Covers setting up model parameters, including optimizer, loss function, and metrics, discussing epochs and testing the model, resulting in a 91% test accuracy after training with 7 epochs and diminishing returns with additional epochs.', 'duration': 459.122, 'highlights': ['The model achieved a 91% test accuracy after training with 7 epochs, showing diminishing returns with additional epochs. Test accuracy reached 91% after training with 7 epochs, indicating diminishing returns with additional epochs.', 'The chapter explains setting up model parameters, including optimizer, loss function, and metrics. Explanation of setting up model parameters, such as optimizer (Adam), loss function (sparse categorical cross entropy), and metrics (accuracy).', "Discussion on the concept of epochs and their impact on the model's training and accuracy. Explanation of epochs and their impact on the training process, including the influence on parameter tweaking and model accuracy.", 'Testing the model on test data to evaluate its accuracy and discussing the need to test on different images. Discussion about testing the model on test data to evaluate accuracy and the importance of testing on different images for reliable results.', 'Introduction to the official tech with tim mugs and encouraging support for the channel through purchases. Introduction to official tech with tim mugs and encouraging support for the channel through purchases.']}], 'duration': 1744.947, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3041723194.jpg', 'highlights': ['The model achieved a 91% test accuracy after training with 7 epochs, showing diminishing returns with additional epochs.', 'The chapter discusses the installation of TensorFlow 2.0, including potential issues with NumPy, and the installation of additional libraries like matplotlib and pandas.', 'The chapter covers data visualization of images using matplotlib The speaker demonstrates using matplotlib to visualize and explain the input and output of images, providing practical examples.', 'The process of flattening data for neural network input is explained using a 28x28 pixel example to generate 784 pixels, enabling the configuration of a flattened input layer for the neural network.', 'The design of the neural network architecture is detailed, featuring a flattened input layer, a hidden layer of 128 neurons, and an output layer of 10 neurons, each representing a different class.']}, {'end': 4217.676, 'segs': [{'end': 3609.47, 'src': 'embed', 'start': 3579.91, 'weight': 0, 'content': [{'end': 3581.331, 'text': 'And obviously this one runs pretty quickly.', 'start': 3579.91, 'duration': 1.421}, {'end': 3582.332, 'text': "So it's not a huge deal.", 'start': 3581.391, 'duration': 0.941}, {'end': 3585.953, 'text': 'Alright, so there we go.', 'start': 3585.373, 'duration': 0.58}, {'end': 3588.875, 'text': 'So now you can see this is actually what our predictions look like.', 'start': 3585.973, 'duration': 2.902}, {'end': 3595.6, 'text': "Now, this is a really weird kind of like looking prediction thing, and we're getting a bunch of different lists.", 'start': 3589.256, 'duration': 6.344}, {'end': 3599.342, 'text': "Now, that's because right, our output layer is 10 neurons.", 'start': 3596.08, 'duration': 3.262}, {'end': 3602.364, 'text': "So we're actually getting an output of 10 different values.", 'start': 3599.522, 'duration': 2.842}, {'end': 3609.47, 'text': 'And these different values are representing how much the model thinks that each picture is a certain class right?', 'start': 3602.705, 'duration': 6.765}], 'summary': 'Neural network outputs 10 values for image classification predictions.', 'duration': 29.56, 'max_score': 3579.91, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3043579910.jpg'}, {'end': 3682.727, 'src': 'embed', 'start': 3655.41, 'weight': 1, 'content': [{'end': 3660.255, 'text': "So what we're actually going to do essentially is we're going to take whatever the highest number is there.", 'start': 3655.41, 'duration': 4.845}, {'end': 3662.738, 'text': "we're going to say that is the predicted value.", 'start': 3660.255, 'duration': 2.483}, {'end': 3671.184, 'text': 'So to do that, what we do is we say np dot arg max, okay, And we just put it around this list.', 'start': 3663.178, 'duration': 8.006}, {'end': 3677.786, 'text': 'Now, what this does is it just gets the largest value and finds like the index of that.', 'start': 3671.745, 'duration': 6.041}, {'end': 3682.727, 'text': 'So in this case, since we have 10 neurons the first one is representing, obviously, t shirt,', 'start': 3677.886, 'duration': 4.841}], 'summary': 'Using np.argmax to predict the highest value in a list of 10 neurons.', 'duration': 27.317, 'max_score': 3655.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3043655410.jpg'}, {'end': 4048.851, 'src': 'embed', 'start': 4015.26, 'weight': 2, 'content': [{'end': 4018.401, 'text': 'It helps me to kind of tweak my lessons and all that as we go forward.', 'start': 4015.26, 'duration': 3.141}, {'end': 4021.742, 'text': 'If you guys enjoyed the video, please leave a like and subscribe and I will see you again in.', 'start': 4018.681, 'duration': 3.061}, {'end': 4045.208, 'text': "Now, in today's video, what we're gonna be doing is talking about text classification with TensorFlow 2.0..", 'start': 4038.803, 'duration': 6.405}, {'end': 4048.851, 'text': "Now what I'm gonna be doing, just to be fully transparent with you guys, here is following,", 'start': 4045.208, 'duration': 3.643}], 'summary': "In today's video, we will discuss text classification with tensorflow 2.0.", 'duration': 33.591, 'max_score': 4015.26, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3044015260.jpg'}], 'start': 3468.181, 'title': 'Machine learning model predictions and text classification', 'summary': 'Covers training and using a machine learning model for predictions, details the use of np.argmax for neural network prediction, and discusses text classification using tensorflow 2.0 with a focus on movie review classification.', 'chapters': [{'end': 3655.07, 'start': 3468.181, 'title': 'Machine learning model predictions', 'summary': 'Discusses training and using a machine learning model to make predictions, including the need to train the model each time, the use of the predict method, and the interpretation of prediction results.', 'duration': 186.889, 'highlights': ["When making a prediction using the model, the input shape needs to be put inside a list or a numpy array, as the predict method expects a group of predictions and gives an output of 10 different values representing the model's confidence for each class.", 'Training the model is necessary each time the program is run, but saving the model for later use is possible, and the process runs quickly.', "The predict method is used to make predictions, and the output layer consists of 10 neurons, with each value representing the model's confidence for a certain class."]}, {'end': 3895.171, 'start': 3655.41, 'title': 'Neural network prediction', 'summary': "Discusses using np.argmax to predict the highest value in a list of neurons, mapping it to a class name, and validating the model's predictions by displaying the images and their respective predicted values from the model.", 'duration': 239.761, 'highlights': ['Using np.argmax to find the index of the highest value in a list of neurons, allowing prediction of the item represented by the neuron.', "Mapping the index of the predicted value to class names to obtain the actual name of the predicted item, enhancing the interpretability of the model's predictions.", "Setting up a loop to display test images and their predicted values, enabling human validation of the model's accuracy by visually comparing the actual images with the model's predictions."]}, {'end': 4217.676, 'start': 3895.171, 'title': 'Text classification with tensorflow 2.0', 'summary': 'Discusses text classification using tensorflow 2.0, following official tutorials, and addresses issues related to data preprocessing and model application, aiming to classify movie reviews as positive or negative.', 'duration': 322.505, 'highlights': ['The chapter discusses text classification using TensorFlow 2.0, following official tutorials, and addresses issues related to data preprocessing and model application, aiming to classify movie reviews as positive or negative. Text classification using TensorFlow 2.0, following official tutorials, addressing data preprocessing and model application, aiming to classify movie reviews as positive or negative.', 'The data is really easy to load in and even pre-processing it like in the last one, we just divided everything by 255. Pre-processing data by dividing everything by 255.', 'The model can classify fashion items like a shirt, a t-shirt, and more within a few minutes, showcasing the ease of creating a simple model. Creating a simple model that can classify fashion items like a shirt, a t-shirt, and more within a few minutes.']}], 'duration': 749.495, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3043468181.jpg', 'highlights': ["The predict method is used to make predictions, and the output layer consists of 10 neurons, with each value representing the model's confidence for a certain class.", 'Using np.argmax to find the index of the highest value in a list of neurons, allowing prediction of the item represented by the neuron.', 'The chapter discusses text classification using TensorFlow 2.0, following official tutorials, and addresses issues related to data preprocessing and model application, aiming to classify movie reviews as positive or negative.']}, {'end': 4968.94, 'segs': [{'end': 4275.956, 'src': 'embed', 'start': 4239.504, 'weight': 0, 'content': [{'end': 4244.026, 'text': "And we're going to do the same thing we did in the previous tutorial, which is just split this into training and testing data.", 'start': 4239.504, 'duration': 4.522}, {'end': 4244.446, 'text': 'So do that?', 'start': 4244.066, 'duration': 0.38}, {'end': 4248.767, 'text': "I'm gonna say train underscore data, train underscore labels, comma,", 'start': 4244.446, 'duration': 4.321}, {'end': 4257.03, 'text': "and then in this case we'll say test underscore data and then test underscore labels, equals, in this case data dot load underscore data.", 'start': 4248.767, 'duration': 8.263}, {'end': 4263.072, 'text': "Now we're just going to add one thing in here, which is num underscore words equals in this case 10, 000.", 'start': 4257.05, 'duration': 6.022}, {'end': 4268.333, 'text': "Now the reason I'm doing this is because this data set contains like a ton of different words.", 'start': 4263.072, 'duration': 5.261}, {'end': 4275.956, 'text': "And what we're going to actually do by saying num words equals 10, 000 is only take the words that are the 10, 000 most frequent,", 'start': 4268.854, 'duration': 7.102}], 'summary': 'Split data into training and testing sets, limit to 10,000 most frequent words.', 'duration': 36.452, 'max_score': 4239.504, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3044239504.jpg'}, {'end': 4373.758, 'src': 'embed', 'start': 4343.147, 'weight': 2, 'content': [{'end': 4349.809, 'text': "Now, this doesn't really look like a movie review to me, does it? Well, what this actually is, is integer encoded words.", 'start': 4343.147, 'duration': 6.662}, {'end': 4353.731, 'text': 'So essentially, each of these integers point to a certain word.', 'start': 4350.229, 'duration': 3.502}, {'end': 4361.633, 'text': "And what we've done just to make it way easier for our model to actually classify these and work with these is we've given each word one integer.", 'start': 4354.031, 'duration': 7.602}, {'end': 4366.455, 'text': 'So in this case, maybe like the word, the integer one stands or something, the integer 14 stands for something.', 'start': 4361.733, 'duration': 4.722}, {'end': 4373.758, 'text': "And all we've done is just added those integers into a list that represents where these words are located in the movie review.", 'start': 4366.835, 'duration': 6.923}], 'summary': 'Transcript explains integer encoding words for easier model classification.', 'duration': 30.611, 'max_score': 4343.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3044343147.jpg'}, {'end': 4679.25, 'src': 'embed', 'start': 4640.51, 'weight': 4, 'content': [{'end': 4647.172, 'text': "So we have like the integer pointing to the word because we're going to have our data set that is going to contain just integers like we've seen here.", 'start': 4640.51, 'duration': 6.662}, {'end': 4651.614, 'text': 'And we want these integers to be able to point to a word as opposed to the other way around.', 'start': 4647.532, 'duration': 4.082}, {'end': 4658.022, 'text': "So what we're doing is just reversing this with a reverse word index list is our dictionary.", 'start': 4651.994, 'duration': 6.028}, {'end': 4659.504, 'text': "So essentially, that's what this is doing here.", 'start': 4658.062, 'duration': 1.442}, {'end': 4664.138, 'text': "Alright, now that we've done that, the last step is just to add a function.", 'start': 4660.495, 'duration': 3.643}, {'end': 4672.484, 'text': 'And what this function will do is actually decode essentially all of this training and testing data into human readable words.', 'start': 4664.498, 'duration': 7.986}, {'end': 4674.326, 'text': "So there's different ways to do this.", 'start': 4673.265, 'duration': 1.061}, {'end': 4679.25, 'text': "Again, I'm just going to take this right from the TensorFlow website, because this part's not super important.", 'start': 4674.726, 'duration': 4.524}], 'summary': 'Reversing word index list to decode data into human readable words.', 'duration': 38.74, 'max_score': 4640.51, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3044640510.jpg'}, {'end': 4913.34, 'src': 'embed', 'start': 4877.324, 'weight': 3, 'content': [{'end': 4883.848, 'text': "And the reason this doesn't work is because we need to know what our inputs shut shape, sorry,", 'start': 4877.324, 'duration': 6.524}, {'end': 4890.232, 'text': 'and size is going to be just like I talked about before we define the input nodes or the input neurons and the output neurons.', 'start': 4883.848, 'duration': 6.384}, {'end': 4895.255, 'text': 'So we have to determine how many input neurons are going to be and how many output neurons is going to be.', 'start': 4890.552, 'duration': 4.703}, {'end': 4903.617, 'text': "Now, if we're like, we don't know how large our data is going to be, and it's different for each, what do you call its entry, then that's an issue.", 'start': 4896.155, 'duration': 7.462}, {'end': 4905.638, 'text': 'So we need to do something to fix that.', 'start': 4903.677, 'duration': 1.961}, {'end': 4913.34, 'text': "So what we're gonna do is we're going to use this padding tag to essentially set a definite length for all of our data.", 'start': 4906.178, 'duration': 7.162}], 'summary': 'Determining input and output neuron count to address variable data size using padding.', 'duration': 36.016, 'max_score': 4877.324, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3044877324.jpg'}], 'start': 4217.776, 'title': 'Text and tensorflow data processing', 'summary': 'Covers loading, preprocessing text data, splitting into training and testing data, and integer encoding words. it also discusses tensorflow data processing, reversing word index, decoding training and testing data, handling varying input lengths with padding, and determining input and output neuron sizes.', 'chapters': [{'end': 4574.167, 'start': 4217.776, 'title': 'Text data preprocessing', 'summary': 'Covers loading and preprocessing text data by splitting it into training and testing data, limiting the number of words to 10,000, and integer encoding words for easier model classification and display.', 'duration': 356.391, 'highlights': ['Splitting the data into training and testing data The process involves splitting the data into train and test data, resulting in variables like train_data, train_labels, test_data, and test_labels.', 'Limiting the number of words to 10,000 The dataset is constrained to 10,000 most frequent words to exclude less frequent words, reducing the complexity of the model and the dataset size.', 'Integer encoding words for easier model classification and display Words are integer encoded to simplify model classification, and a dictionary, word_index, is created to map integers to their respective words for display and analysis.']}, {'end': 4968.94, 'start': 4574.167, 'title': 'Tensorflow data processing', 'summary': 'Discusses data processing in tensorflow, including reversing word index, decoding training and testing data, handling varying input lengths with padding, and determining input and output neuron sizes.', 'duration': 394.773, 'highlights': ['Handling varying input lengths with padding The speaker discusses the challenge of varying input lengths and proposes using a padding tag to set a definite length for all data, such as choosing 250 as the maximum length and adding padding tags to shorter reviews.', 'Reversing word index for data set The speaker explains the process of reversing the word index to have a dictionary where integers point to words, essential for the data set that contains just integers.', 'Decoding training and testing data The chapter introduces a function to decode training and testing data into human-readable words using the reverse word index, handling unknown characters and default values.', 'Determining input and output neuron sizes The speaker emphasizes the need to determine input and output neuron sizes to address the issue of varying data sizes, ensuring a definite length for all data.']}], 'duration': 751.164, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3044217776.jpg', 'highlights': ['Splitting the data into training and testing data The process involves splitting the data into train and test data, resulting in variables like train_data, train_labels, test_data, and test_labels.', 'Limiting the number of words to 10,000 The dataset is constrained to 10,000 most frequent words to exclude less frequent words, reducing the complexity of the model and the dataset size.', 'Integer encoding words for easier model classification and display Words are integer encoded to simplify model classification, and a dictionary, word_index, is created to map integers to their respective words for display and analysis.', 'Handling varying input lengths with padding The speaker discusses the challenge of varying input lengths and proposes using a padding tag to set a definite length for all data, such as choosing 250 as the maximum length and adding padding tags to shorter reviews.', 'Reversing word index for data set The speaker explains the process of reversing the word index to have a dictionary where integers point to words, essential for the data set that contains just integers.', 'Decoding training and testing data The chapter introduces a function to decode training and testing data into human-readable words using the reverse word index, handling unknown characters and default values.', 'Determining input and output neuron sizes The speaker emphasizes the need to determine input and output neuron sizes to address the issue of varying data sizes, ensuring a definite length for all data.']}, {'end': 6189.78, 'segs': [{'end': 4994.196, 'src': 'embed', 'start': 4969, 'weight': 0, 'content': [{'end': 4974.325, 'text': "So for me, it doesn't really make sense to just retype them out when I can just use these kind of fancy tools.", 'start': 4969, 'duration': 5.325}, {'end': 4978.268, 'text': "So what we're going to say is we're going to redefine our training and testing data.", 'start': 4974.845, 'duration': 3.423}, {'end': 4984.492, 'text': "And what we're going to do is just trim that data so that it's only at or kind of normalize that data.", 'start': 4978.788, 'duration': 5.704}, {'end': 4986.232, 'text': "So it's at 250 words.", 'start': 4984.512, 'duration': 1.72}, {'end': 4994.196, 'text': "So to do that, I'm going to say train underscore data equals in this case, Kara's got pre raw sassing.", 'start': 4986.733, 'duration': 7.463}], 'summary': 'Redefining training and testing data to 250 words using tools.', 'duration': 25.196, 'max_score': 4969, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3044969000.jpg'}, {'end': 5122.954, 'src': 'embed', 'start': 5090.888, 'weight': 1, 'content': [{'end': 5093.849, 'text': "that's integer, encoded it, decodes it and then it.", 'start': 5090.888, 'duration': 2.961}, {'end': 5096.29, 'text': 'we can print that information out to the screen to have a look at it.', 'start': 5093.849, 'duration': 2.441}, {'end': 5100.273, 'text': "what we've just done now is we've done what's called pre processing our data,", 'start': 5096.67, 'duration': 3.603}, {'end': 5103.937, 'text': 'which means just making it into a form that our model can actually accept.', 'start': 5100.273, 'duration': 3.664}, {'end': 5104.938, 'text': "And that's consistent.", 'start': 5104.017, 'duration': 0.921}, {'end': 5108.081, 'text': "And that's what you're always going to want to do with any data that you have.", 'start': 5104.958, 'duration': 3.123}, {'end': 5113.105, 'text': "Typically it's going to take you a bit more work than what we have, because it's only two lines to pre process our data,", 'start': 5108.501, 'duration': 4.604}, {'end': 5114.747, 'text': "because Kara's kind of does it for us.", 'start': 5113.105, 'duration': 1.642}, {'end': 5117.809, 'text': "But for the purpose of this example, that's fine.", 'start': 5115.527, 'duration': 2.282}, {'end': 5122.954, 'text': "Alright, so now that we've done that, it's actually time to define our model.", 'start': 5118.75, 'duration': 4.204}], 'summary': "Preprocessed data for model; kara's simplifies process.", 'duration': 32.066, 'max_score': 5090.888, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3045090888.jpg'}, {'end': 5825.288, 'src': 'embed', 'start': 5795.859, 'weight': 2, 'content': [{'end': 5798.841, 'text': "we're going to talk about this a bit more with another drawing of the whole network.", 'start': 5795.859, 'duration': 2.982}, {'end': 5806.346, 'text': "But I hope you're getting the idea that the whole point of this embedding layer is to make word vectors that and then group those word vectors or kind of like,", 'start': 5799.081, 'duration': 7.265}, {'end': 5810.348, 'text': 'make them close together, based on words that are similar and that are different.', 'start': 5806.346, 'duration': 4.002}, {'end': 5816.893, 'text': 'So again, just like we would have grading good here, we would hope that a word vector like bad would be down here,', 'start': 5810.668, 'duration': 6.225}, {'end': 5822.637, 'text': 'where it has a big difference from great and good, so that we can tell that these words are not related whatsoever.', 'start': 5816.893, 'duration': 5.744}, {'end': 5825.288, 'text': "Alright, so that's how the embedding layer works.", 'start': 5823.567, 'duration': 1.721}], 'summary': 'The embedding layer groups word vectors based on similarity and difference.', 'duration': 29.429, 'max_score': 5795.859, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3045795859.jpg'}, {'end': 6082.907, 'src': 'embed', 'start': 6059.46, 'weight': 3, 'content': [{'end': 6066.762, 'text': 'So what we end up having is this embedding layer which is going to have all these word vectors that represent different words.', 'start': 6059.46, 'duration': 7.302}, {'end': 6071.523, 'text': 'we average them out, we pass them into this 16 neuron layer.', 'start': 6066.762, 'duration': 4.761}, {'end': 6078.645, 'text': 'that then goes into an output layer which will spit out a value between zero and one using the sigmoid function,', 'start': 6071.523, 'duration': 7.122}, {'end': 6082.907, 'text': 'which I believe I have to correct myself because in other videos I said it did between negative one and one.', 'start': 6078.645, 'duration': 4.262}], 'summary': 'Neural network uses word vectors to output values between 0 and 1.', 'duration': 23.447, 'max_score': 6059.46, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046059460.jpg'}], 'start': 4969, 'title': 'Text data processing for nlp', 'summary': 'Covers data trimming and normalization, text data preprocessing, word embedding, and neural network text classification for nlp, aiming to redefine and preprocess data, create word vectors, and achieve accurate text classification.', 'chapters': [{'end': 5050.414, 'start': 4969, 'title': 'Data trimming and normalization for nlp', 'summary': "Discusses redefining training and testing data by trimming and normalizing it to 250 words using kara's pre raw sassing sequence pad function, with parameters such as pad value and pad type, aiming to make all values equal.", 'duration': 81.414, 'highlights': ["The chapter discusses redefining training and testing data by trimming and normalizing it to 250 words using Kara's pre raw sassing sequence pad function, with parameters such as pad value and pad type, aiming to make all values equal.", 'Using the sequence pad function, the transcript mentions defining parameters such as pad value and pad type for the train data, aiming to pad the NumPy array to make all values equal.', "The speaker mentions setting the padding type as 'post' and choosing a maximum length to make all values equal."]}, {'end': 5372.367, 'start': 5050.414, 'title': 'Text data preprocessing and model definition', 'summary': 'Covers the preprocessing of text data to make it consistent for model input, defining the model architecture with specific layers and explaining their functions, with a demonstration of maintaining consistent data length and output neuron usage.', 'duration': 321.953, 'highlights': ['The chapter covers the preprocessing of text data to make it consistent for model input, defining the model architecture with specific layers and explaining their functions. The speaker discusses the preprocessing of text data to ensure it is in a form that the model can accept, emphasizing the importance of consistency in data formatting and length, and then proceeds to define the model architecture.', 'A demonstration of maintaining consistent data length and output neuron usage is provided. The speaker demonstrates maintaining consistent data length for both train and test data by keeping them at a length of 250, then explains the usage of one output neuron for determining the probability of a review being good or bad using the sigmoid function.']}, {'end': 5925.527, 'start': 5373.008, 'title': 'Word embedding for neural networks', 'summary': 'Explains the process of word embedding in neural networks, aiming to group words with similar meanings together by creating word vectors in a 16-dimensional space, and then scaling down the dimensions using global average pooling 1d layer.', 'duration': 552.519, 'highlights': ['The chapter explains the process of word embedding in neural networks The transcript provides insights into the process of word embedding in neural networks, aiming to capture the meaning of words and group similar words together.', 'Creating word vectors in a 16-dimensional space It describes the creation of word vectors in a 16-dimensional space to represent each word, allowing the neural network to understand the context and meaning of words.', 'Scaling down dimensions using global average pooling 1d layer It explains the process of scaling down the 16-dimensional word vectors to make it easier to compute and train the network using the global average pooling 1d layer.']}, {'end': 6189.78, 'start': 5925.647, 'title': 'Neural network text classification', 'summary': 'Explains the process of feeding an input sequence into an embedding layer, averaging the vectors, passing them through dense layers, and using the sigmoid function for classification in a neural network with 16 neurons, all to achieve accurate text classification.', 'duration': 264.133, 'highlights': ['Feeding the input sequence into the embedding layer to find the representation of words in the embedding layer, which contains vectors for each word in the vocabulary.', 'Averaging the vectors in the next layer to shrink the data down and then passing them through dense layers with 16 neurons for classification.', 'The dense layer attempts to classify patterns of words using weights and biases to give an accurate classification, followed by compiling and training the model using the atom optimizer.', 'The model is compiled with the atom optimizer, loss function, and metrics defined for training the neural network for text classification.']}], 'duration': 1220.78, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3044969000.jpg', 'highlights': ["The chapter discusses redefining training and testing data by trimming and normalizing it to 250 words using Kara's pre raw sassing sequence pad function, with parameters such as pad value and pad type, aiming to make all values equal.", 'The chapter covers the preprocessing of text data to make it consistent for model input, defining the model architecture with specific layers and explaining their functions, emphasizing the importance of consistency in data formatting and length, and then proceeds to define the model architecture.', 'The chapter explains the process of word embedding in neural networks, aiming to capture the meaning of words and group similar words together, and describes the creation of word vectors in a 16-dimensional space to represent each word, allowing the neural network to understand the context and meaning of words.', 'Feeding the input sequence into the embedding layer to find the representation of words in the embedding layer, which contains vectors for each word in the vocabulary, followed by averaging the vectors in the next layer to shrink the data down and then passing them through dense layers with 16 neurons for classification, and compiling and training the model using the atom optimizer.']}, {'end': 6690.952, 'segs': [{'end': 6221.025, 'src': 'embed', 'start': 6190.361, 'weight': 7, 'content': [{'end': 6197.203, 'text': "And then for the loss function, where you're going to use the binary underscore cross entropy.", 'start': 6190.361, 'duration': 6.842}, {'end': 6201.787, 'text': 'Now, what this one essentially is is well, binary means like two options, right?', 'start': 6197.663, 'duration': 4.124}, {'end': 6206.632, 'text': 'And in our case we want to have two options for the output neuron, which is zero or one.', 'start': 6202.048, 'duration': 4.584}, {'end': 6212.438, 'text': "So what's actually happening here is we have the sigmoid function, which means our numbers going to be between zero and one.", 'start': 6207.093, 'duration': 5.345}, {'end': 6221.025, 'text': 'But what the loss function will do is pretty well calculate the difference between for example, say our output neuron is like 0.2.', 'start': 6212.798, 'duration': 8.227}], 'summary': 'Using binary cross entropy loss function to handle two output options and calculate the difference.', 'duration': 30.664, 'max_score': 6190.361, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046190361.jpg'}, {'end': 6259.341, 'src': 'embed', 'start': 6235.272, 'weight': 0, 'content': [{'end': 6241.595, 'text': "And they're not like, I mean, they are important, but not to really like memorize per se, like you kind of just mess with different ones.", 'start': 6235.272, 'duration': 6.323}, {'end': 6246.577, 'text': 'But in this case, binary cross entropy works well, because we have two possible values 01.', 'start': 6241.895, 'duration': 4.682}, {'end': 6252.859, 'text': "So, rather than using the other one that we used before, which I don't even remember what it was called something cross entropy,", 'start': 6246.577, 'duration': 6.282}, {'end': 6254.179, 'text': "we're using binary cross entropy.", 'start': 6252.859, 'duration': 1.32}, {'end': 6259.341, 'text': "Okay, so now what we're going to do is we're actually going to split our training data into two sets.", 'start': 6254.199, 'duration': 5.142}], 'summary': 'Using binary cross entropy for two possible values 01, then splitting training data into two sets.', 'duration': 24.069, 'max_score': 6235.272, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046235272.jpg'}, {'end': 6291.945, 'src': 'embed', 'start': 6268.785, 'weight': 5, 'content': [{'end': 6276.569, 'text': "And what validation data is, is essentially we can check how well our model is performing based on the tunes and tweaks we're doing,", 'start': 6268.785, 'duration': 7.784}, {'end': 6278.55, 'text': 'on the training data, on new data.', 'start': 6276.569, 'duration': 1.981}, {'end': 6283.153, 'text': 'Now. the reason we do that is so that we can get a more accurate sense of how well our model is,', 'start': 6278.83, 'duration': 4.323}, {'end': 6291.945, 'text': "because we're going to be testing new data to get the accuracy each time, rather than testing it on data that we already seen before,", 'start': 6284.113, 'duration': 7.832}], 'summary': 'Validation data checks model performance on new data for more accurate results.', 'duration': 23.16, 'max_score': 6268.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046268785.jpg'}, {'end': 6339.263, 'src': 'embed', 'start': 6303.5, 'weight': 2, 'content': [{'end': 6308.425, 'text': "So we're going to do is we're going to say x underscore, Val equals, and all we're going to do is just grab the train data.", 'start': 6303.5, 'duration': 4.925}, {'end': 6312.309, 'text': "we're just going to cut it to 1000 or 10, 000 entries.", 'start': 6308.425, 'duration': 3.884}, {'end': 6317.714, 'text': "So there's actually 25, 000 entries, or I guess reviews in our training data.", 'start': 6312.669, 'duration': 5.045}, {'end': 6321.498, 'text': "So we're just going to take 10, 000 of it and say we're going to use that as validation data.", 'start': 6317.734, 'duration': 3.764}, {'end': 6325.499, 'text': "Now in terms of the size of validation data, it doesn't really matter that much.", 'start': 6321.838, 'duration': 3.661}, {'end': 6328.18, 'text': 'This is what TensorFlow is using.', 'start': 6326.159, 'duration': 2.021}, {'end': 6329.26, 'text': "So I'm just kind of going with that.", 'start': 6328.22, 'duration': 1.04}, {'end': 6332.061, 'text': 'But again, mess with these numbers and see what happens to your model.', 'start': 6329.56, 'duration': 2.501}, {'end': 6339.263, 'text': "Everything with our neural networks and machine learning really is going to come down to very fine what's known as hyper parameters,", 'start': 6332.241, 'duration': 7.022}], 'summary': 'Using 10,000 entries out of 25,000 for validation data in tensorflow to fine-tune hyperparameters.', 'duration': 35.763, 'max_score': 6303.5, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046303500.jpg'}, {'end': 6409.46, 'src': 'embed', 'start': 6380.734, 'weight': 4, 'content': [{'end': 6386.116, 'text': "And then we're just going to use the the training stuff for the validation data to validate the model.", 'start': 6380.734, 'duration': 5.382}, {'end': 6389.417, 'text': "Alright, so now that we've done that, it is actually time to fit the model.", 'start': 6386.616, 'duration': 2.801}, {'end': 6393.539, 'text': "So I'm just gonna say, like, fit model.", 'start': 6389.517, 'duration': 4.022}, {'end': 6398.001, 'text': "And you'll see why I'd named this something different in a second is going to be equal to model dot fit.", 'start': 6394.26, 'duration': 3.741}, {'end': 6404.885, 'text': "And in this case, what we're going to do is going to say x underscore train, y underscore train, we're going to say epochs.", 'start': 6398.542, 'duration': 6.343}, {'end': 6409.46, 'text': 'is equal to angle to spell 40.', 'start': 6407.279, 'duration': 2.181}], 'summary': 'Using training data to validate the model with 40 epochs', 'duration': 28.726, 'max_score': 6380.734, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046380734.jpg'}, {'end': 6488.386, 'src': 'embed', 'start': 6455.744, 'weight': 8, 'content': [{'end': 6460.828, 'text': "because the thing is it's kind of I mean we're loading all of our reviews into memory.", 'start': 6455.744, 'duration': 5.084}, {'end': 6464.47, 'text': "But In some cases, we won't be able to do that.", 'start': 6460.848, 'duration': 3.622}, {'end': 6468.953, 'text': "And we won't be able to like feed the model all of our reviews on each single cycle.", 'start': 6464.49, 'duration': 4.463}, {'end': 6474.397, 'text': "So we just set up a batch size, that's going to define essentially how many at once we're going to give.", 'start': 6469.273, 'duration': 5.124}, {'end': 6477.699, 'text': "And I know I'm kind of horribly explaining what a batch size is.", 'start': 6475.337, 'duration': 2.362}, {'end': 6488.386, 'text': "But we'll get into more on batch sizes and how we can kind of do like buffering through our data and like going taking some from a text file and reading into memory in later videos,", 'start': 6478.359, 'duration': 10.027}], 'summary': 'Setting a batch size to define how many reviews are fed into the model at once for efficient processing.', 'duration': 32.642, 'max_score': 6455.744, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046455744.jpg'}, {'end': 6613.286, 'src': 'embed', 'start': 6587.665, 'weight': 1, 'content': [{'end': 6592.487, 'text': "And yeah, I mean, that's the model we tested and it's 87% accurate.", 'start': 6587.665, 'duration': 4.822}, {'end': 6596.21, 'text': "So now let's actually have let's interpret some of these results a little bit better.", 'start': 6592.768, 'duration': 3.442}, {'end': 6598.591, 'text': "And let's show some reviews,", 'start': 6596.23, 'duration': 2.361}, {'end': 6604.515, 'text': "let's do a prediction on some of the reviews and then see like if this our model kind of makes sense for what's going on here.", 'start': 6598.591, 'duration': 5.924}, {'end': 6608.3, 'text': "So what I'm gonna do is I am just going to actually just copy some output that I have here.", 'start': 6604.955, 'duration': 3.345}, {'end': 6611.965, 'text': 'Just save us a bit of time because I am going to wrap up the video in a minute here.', 'start': 6609.361, 'duration': 2.604}, {'end': 6613.286, 'text': 'But essentially what this does?', 'start': 6612.265, 'duration': 1.021}], 'summary': 'Model tested at 87% accuracy for review prediction.', 'duration': 25.621, 'max_score': 6587.665, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046587665.jpg'}], 'start': 6190.361, 'title': 'Neural network training', 'summary': 'Covers the use of binary cross entropy as the loss function, splitting training data for model validation, and text classification, achieving 87% accuracy on test data, and interpreting model results.', 'chapters': [{'end': 6254.179, 'start': 6190.361, 'title': 'Loss function and binary cross entropy', 'summary': 'Covers the use of binary cross entropy as the loss function for a neural network, which is effective for calculating the difference between the predicted output and the actual value, particularly suited for scenarios with two possible values (0 and 1).', 'duration': 63.818, 'highlights': ["The loss function binary cross entropy is used for calculating the difference between the output neuron's value and the actual answer, particularly suited for scenarios with two possible values (0 and 1).", 'Binary cross entropy is preferred over other loss functions due to its effectiveness in scenarios with two possible values (0 and 1).', "The sigmoid function ensures the output neuron's value is between 0 and 1."]}, {'end': 6438.472, 'start': 6254.199, 'title': 'Splitting training data and model validation', 'summary': 'Discusses the process of splitting training data into validation data for model performance evaluation, highlighting the significance of hyper parameters and the fitting of the model with specific parameters and validation data.', 'duration': 184.273, 'highlights': ["The chapter emphasizes the importance of validation data to check the model's performance based on tuning, stating that 10,000 entries out of 25,000 are used for validation data. Validation data is crucial to evaluate model performance; 10,000 out of 25,000 entries used for validation data.", 'The significance of hyper parameters and hyper tuning in neural networks and machine learning is highlighted, emphasizing the iterative process of changing individual parameters to achieve a better and more accurate model. Emphasis on the significance of hyper parameters and hyper tuning in neural networks and machine learning.', 'The process of fitting the model is detailed, indicating the use of specific parameters such as epochs, batch size, and validation data (x_val and y_val). The chapter encourages experimentation with different parameter values for model evaluation. Detailed process of fitting the model with specific parameters and validation data; encouragement for experimentation with different parameter values.']}, {'end': 6690.952, 'start': 6438.813, 'title': 'Neural networks: text classification', 'summary': 'Discusses the concept of batch size, model evaluation, and accuracy, demonstrating a 87% accuracy on the test data and the importance of test and validation data. it also covers interpreting model results and making predictions on reviews.', 'duration': 252.139, 'highlights': ['The model achieved an accuracy of 87% on the test data. The accuracy of the model on the test data was 87%, demonstrating its performance in text classification.', "The significance of test and validation data is emphasized for ensuring the model's correctness. The chapter emphasizes the importance of test and validation data in ensuring the correctness of the model's performance, highlighting the need to validate the model on new data.", "Demonstrates making predictions on reviews and interpreting model results. The chapter demonstrates the process of making predictions on reviews using the model and emphasizes the need to interpret the results to understand the model's performance.", 'Batch size concept is introduced for loading data into memory during model training. The concept of batch size is introduced, explaining its role in loading data into memory during model training and its significance in managing large datasets.', 'Explanation of batch size and its role in defining the amount of data loaded at once. The chapter provides an explanation of batch size, defining its role in determining the amount of data loaded at once during model training.']}], 'duration': 500.591, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046190361.jpg', 'highlights': ["Binary cross entropy is used for calculating the difference between the output neuron's value and the actual answer, particularly suited for scenarios with two possible values (0 and 1).", 'The model achieved an accuracy of 87% on the test data, demonstrating its performance in text classification.', 'The significance of hyper parameters and hyper tuning in neural networks and machine learning is highlighted, emphasizing the iterative process of changing individual parameters to achieve a better and more accurate model.', "The chapter emphasizes the importance of validation data to check the model's performance based on tuning, stating that 10,000 entries out of 25,000 are used for validation data.", 'The process of fitting the model is detailed, indicating the use of specific parameters such as epochs, batch size, and validation data (x_val and y_val).', "The significance of test and validation data is emphasized for ensuring the model's correctness, highlighting the need to validate the model on new data.", "The chapter demonstrates the process of making predictions on reviews using the model and emphasizes the need to interpret the results to understand the model's performance.", "The sigmoid function ensures the output neuron's value is between 0 and 1.", 'The concept of batch size is introduced, explaining its role in loading data into memory during model training and its significance in managing large datasets.']}, {'end': 7996.294, 'segs': [{'end': 6800.473, 'src': 'embed', 'start': 6765.743, 'weight': 0, 'content': [{'end': 6770.005, 'text': "So in today's video, we're gonna be doing is talking about saving and loading our models.", 'start': 6765.743, 'duration': 4.262}, {'end': 6774.966, 'text': "And then we're going to be doing a prediction on some data that doesn't come from this actual data set.", 'start': 6770.365, 'duration': 4.601}, {'end': 6778.427, 'text': 'Now I know this might seem kind of trivial, we already know how to do predictions.', 'start': 6775.546, 'duration': 2.881}, {'end': 6781.308, 'text': 'But trust me when I tell you this is a lot harder than it looks.', 'start': 6778.787, 'duration': 2.521}, {'end': 6789.67, 'text': "Because if we're just taking in string data, that means we have to actually do the encoding of the pre processing, removing certain characters,", 'start': 6781.648, 'duration': 8.022}, {'end': 6794.491, 'text': 'making sure that that data looks the same as the data that our neural network is expecting,', 'start': 6789.67, 'duration': 4.821}, {'end': 6800.473, 'text': 'which in this case is a list of encoded numbers right or have encoded words, that is, essentially just numbers.', 'start': 6794.491, 'duration': 5.982}], 'summary': 'Discussing saving/loading models and predicting with new data, emphasizing encoding challenges.', 'duration': 34.73, 'max_score': 6765.743, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046765743.jpg'}, {'end': 6844.032, 'src': 'embed', 'start': 6818.937, 'weight': 2, 'content': [{'end': 6824.218, 'text': "So what you want to do is, when you're done training the model, you want to save it, or sometimes you even want to save it,", 'start': 6818.937, 'duration': 5.281}, {'end': 6825.998, 'text': 'like halfway through a training process.', 'start': 6824.218, 'duration': 1.78}, {'end': 6830.659, 'text': 'This is known as checkpointing the model so that you can go back and continue to train it later.', 'start': 6826.438, 'duration': 4.221}, {'end': 6834.443, 'text': "Now in this video, we're just going to talk about saving the model once it's completely finished.", 'start': 6831.019, 'duration': 3.424}, {'end': 6836.925, 'text': 'But in future videos, when we have larger networks,', 'start': 6834.703, 'duration': 2.222}, {'end': 6844.032, 'text': 'we will talk about checkpointing and how you know how to load your or train your model in like batches with different size data and all that.', 'start': 6836.925, 'duration': 7.107}], 'summary': 'Save model after training; discuss checkpointing for larger networks later.', 'duration': 25.095, 'max_score': 6818.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046818937.jpg'}, {'end': 6877.508, 'src': 'embed', 'start': 6851.559, 'weight': 4, 'content': [{'end': 6858.081, 'text': "Now, the reason I'm doing that is just because, for our next exercise, which is going to be making predictions on outside data,", 'start': 6851.559, 'duration': 6.522}, {'end': 6861.562, 'text': 'we want to have as many words in our model as possible,', 'start': 6858.081, 'duration': 3.481}, {'end': 6866.204, 'text': "so that when it gets kind of some weirder words that aren't that common and knows what to do with them.", 'start': 6861.562, 'duration': 4.642}, {'end': 6867.064, 'text': "So I've done a few tests.", 'start': 6866.244, 'duration': 0.82}, {'end': 6871.906, 'text': 'And I noticed that with the, what do you call it with the vocabulary size bumped up, it performs a little bit better.', 'start': 6867.104, 'duration': 4.802}, {'end': 6872.626, 'text': "So we're going to do that.", 'start': 6871.926, 'duration': 0.7}, {'end': 6874.987, 'text': 'So anyways, we bumped the vocabulary size.', 'start': 6873.346, 'duration': 1.641}, {'end': 6877.508, 'text': 'And now after we train the model, we need to save it.', 'start': 6875.427, 'duration': 2.081}], 'summary': 'Increasing vocabulary size improves model performance for making predictions on outside data.', 'duration': 25.949, 'max_score': 6851.559, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046851559.jpg'}, {'end': 7294.671, 'src': 'embed', 'start': 7264.42, 'weight': 5, 'content': [{'end': 7266.322, 'text': "But that's all we need to do to remove everything.", 'start': 7264.42, 'duration': 1.902}, {'end': 7270.068, 'text': 'And now we actually need to encode and trim our data down to 250 words.', 'start': 7266.643, 'duration': 3.425}, {'end': 7272.47, 'text': 'So to encode our data.', 'start': 7271.289, 'duration': 1.181}, {'end': 7276.314, 'text': "I'm going to say encode equals in this case, and we're just literally.", 'start': 7272.47, 'duration': 3.844}, {'end': 7280.558, 'text': "we'll make a function called like review underscore encode.", 'start': 7276.314, 'duration': 4.244}, {'end': 7283.061, 'text': "And we'll pass in our end line.", 'start': 7281.019, 'duration': 2.042}, {'end': 7290.589, 'text': 'Now what review and code will do is look up the mappings for all of the words and return to us an encoded list.', 'start': 7284.002, 'duration': 6.587}, {'end': 7294.671, 'text': "And then, finally, what we're going to do and we'll create this function in just a second, don't worry,", 'start': 7291.129, 'duration': 3.542}], 'summary': 'Encode and trim data to 250 words using review_encode function.', 'duration': 30.251, 'max_score': 7264.42, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3047264420.jpg'}, {'end': 7720.405, 'src': 'embed', 'start': 7687.367, 'weight': 6, 'content': [{'end': 7691.45, 'text': 'So quickly before we go forward and you guys get frustrated with not being able to install this.', 'start': 7687.367, 'duration': 4.083}, {'end': 7695.853, 'text': 'make sure that you have a graphics card that actually works for this program or for this module.', 'start': 7691.45, 'duration': 4.403}, {'end': 7701.596, 'text': 'That means you have to have a graphics card that is a GTX 10 50 TI or higher.', 'start': 7696.213, 'duration': 5.383}, {'end': 7707.959, 'text': "Those are the ones that are listed on TensorFlow's website as compatible with TensorFlow 2.0 GPU.", 'start': 7701.916, 'duration': 6.043}, {'end': 7711.681, 'text': 'If you want to have a quick thing without having to go to the website to see if yours works.', 'start': 7708.259, 'duration': 3.422}, {'end': 7720.405, 'text': 'if it has four gigs of video Ram and is a GTX generation card or higher, it most likely works with TensorFlow 2.0..', 'start': 7711.681, 'duration': 8.724}], 'summary': 'To install tensorflow 2.0 gpu, ensure a gtx 1050 ti or higher graphics card with 4gb video ram.', 'duration': 33.038, 'max_score': 7687.367, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3047687367.jpg'}, {'end': 7750.647, 'src': 'embed', 'start': 7724.59, 'weight': 7, 'content': [{'end': 7731.357, 'text': 'But any 1060, 1070, 1080 or RTX cards that have CUDA cores on them will work for this.', 'start': 7724.59, 'duration': 6.767}, {'end': 7736.942, 'text': 'Essentially, you just need a CUDA enabled GPU so you can check if yours meets that requirement before moving forward.', 'start': 7731.477, 'duration': 5.465}, {'end': 7741.344, 'text': "Now to do this, I'm just gonna be following the steps listed on the TensorFlow website.", 'start': 7737.643, 'duration': 3.701}, {'end': 7743.885, 'text': 'Now you may run into some issues while doing this.', 'start': 7741.864, 'duration': 2.021}, {'end': 7746.826, 'text': 'But for Ubuntu, this is pretty straightforward.', 'start': 7744.665, 'duration': 2.161}, {'end': 7750.647, 'text': "And I'm essentially just gonna be copying these commands and pasting them in my terminal.", 'start': 7746.846, 'duration': 3.801}], 'summary': 'Cuda enabled gpus like 1060, 1070, 1080, or rtx cards work for tensorflow installation', 'duration': 26.057, 'max_score': 7724.59, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3047724590.jpg'}], 'start': 6691.272, 'title': 'Model saving and loading in machine learning', 'summary': 'Discusses the process of saving and loading machine learning models, focusing on prediction speed, challenges of processing string data, and the significance of text preprocessing. it also covers saving and loading trained models in keras, adjusting vocabulary size, and installing tensorflow 2.0 gpu version on ubuntu.', 'chapters': [{'end': 6800.473, 'start': 6691.272, 'title': 'Model saving and loading in machine learning', 'summary': 'Discusses the process of saving and loading machine learning models to avoid repetitive training, with a focus on prediction speed and the challenges of processing string data, aiming for long training times in future models.', 'duration': 109.201, 'highlights': ['The process of saving and loading machine learning models is discussed to avoid repetitive training, with an emphasis on prediction speed and efficiency.', 'Future models are expected to have longer training times, possibly taking a few days or hours to train, highlighting the need to save and load models for efficient prediction.', 'Challenges of processing string data for predictions are highlighted, including encoding, pre-processing, and ensuring data consistency for the neural network.']}, {'end': 7080.784, 'start': 6801.013, 'title': 'Saving and loading trained models in keras', 'summary': 'Discusses the process of saving a trained model in keras, including the benefits of saving and loading models, the method for saving a model, and the process for loading a model. it also covers the significance of adjusting the vocabulary size for making predictions on outside data and the testing of the model on an external text file.', 'duration': 279.771, 'highlights': ['The process of saving a trained model in Keras is discussed, emphasizing the benefits of saving and loading models, such as avoiding the need to retrain the model for each prediction and enabling the retention of model checkpoints for continued training. This process eliminates the need to retrain the model for each prediction, providing convenience, especially for larger models that require significant training time. Additionally, the concept of checkpointing the model is introduced, allowing for the retention of model checkpoints to continue training at a later point.', "The method for saving a trained model in Keras is demonstrated, showcasing the simplicity of the process by using the 'model.save' function and specifying the desired name and extension for the saved model. The method involves using the 'model.save' function with the desired name and extension, such as 'model.h5', to save the model in binary data, facilitating quick retrieval and utilization for making predictions.", 'The significance of adjusting the vocabulary size for making predictions on outside data is addressed, emphasizing the importance of having a comprehensive vocabulary to handle less common words effectively. The adjustment of the vocabulary size to 88,000 is highlighted as essential for making predictions on outside data, ensuring the model can effectively handle less common words and improve performance.', 'The testing of the model on an external text file is described, focusing on the process of loading the text file and converting its content into a format suitable for the model to utilize. The process involves loading a text file, converting its content into a usable format for the model, and exemplifies the practical application of testing the model on external data, in this case, a movie review from IMDb.']}, {'end': 7538.492, 'start': 7081.145, 'title': 'Text preprocessing for model prediction', 'summary': 'Outlines the process of preprocessing text data to feed it into a model, including cleaning and encoding the text, and limiting the size of the input to 250 words, and then using a trained model to make predictions.', 'duration': 457.347, 'highlights': ['Explaining the process of cleaning and encoding the text data to prepare it for the model, ensuring a maximum size of 250 words for input during training. Maximum size of text set at 250 words.', 'Demonstrating the creation of a function to encode the text data and applying pre-processing steps to the encoded data for model input. Creation of a function to encode the text data.', 'Identifying an error in the code and rectifying it, leading to successful execution of the model prediction process. Successful execution of the model prediction process.']}, {'end': 7996.294, 'start': 7538.592, 'title': 'Installing tensorflow 2.0 gpu version on ubuntu', 'summary': 'Demonstrates the installation of tensorflow 2.0 gpu version on ubuntu, emphasizing the necessity of a compatible graphics card and the step-by-step process of installing cuda, nvidia drivers, tensorrt, and tensorflow 2.0, achieving a successful installation and validation.', 'duration': 457.702, 'highlights': ['The chapter emphasizes the necessity of having a compatible graphics card, specifically a GTX 1050 TI or higher, for installing TensorFlow 2.0 GPU version, ensuring efficient performance and compatibility with the program.', 'The step-by-step process of installing CUDA, NVIDIA drivers, TensorRT, and TensorFlow 2.0 is detailed, providing a comprehensive guide for successfully installing the GPU version on Ubuntu.', 'The successful installation and validation of TensorFlow 2.0 GPU version is highlighted, indicating the completion of the installation process and the importance of validating the installation by importing TensorFlow without encountering errors in Python 3.']}], 'duration': 1305.022, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/6g4O5UOH304/pics/6g4O5UOH3046691272.jpg', 'highlights': ['The process of saving and loading machine learning models is discussed to avoid repetitive training, with an emphasis on prediction speed and efficiency.', 'Challenges of processing string data for predictions are highlighted, including encoding, pre-processing, and ensuring data consistency for the neural network.', 'The process of saving a trained model in Keras is discussed, emphasizing the benefits of saving and loading models, such as avoiding the need to retrain the model for each prediction and enabling the retention of model checkpoints for continued training.', "The method for saving a trained model in Keras is demonstrated, showcasing the simplicity of the process by using the 'model.save' function and specifying the desired name and extension for the saved model.", 'The significance of adjusting the vocabulary size for making predictions on outside data is addressed, emphasizing the importance of having a comprehensive vocabulary to handle less common words effectively.', 'Explaining the process of cleaning and encoding the text data to prepare it for the model, ensuring a maximum size of 250 words for input during training.', 'The chapter emphasizes the necessity of having a compatible graphics card, specifically a GTX 1050 TI or higher, for installing TensorFlow 2.0 GPU version, ensuring efficient performance and compatibility with the program.', 'The step-by-step process of installing CUDA, NVIDIA drivers, TensorRT, and TensorFlow 2.0 is detailed, providing a comprehensive guide for successfully installing the GPU version on Ubuntu.']}], 'highlights': ['The model achieved a 91% test accuracy after training with 7 epochs, showing diminishing returns with additional epochs.', 'The process of saving and loading machine learning models is discussed to avoid repetitive training, with an emphasis on prediction speed and efficiency.', 'The process of fitting the model is detailed, indicating the use of specific parameters such as epochs, batch size, and validation data (x_val and y_val).', 'The process of training a neural network is exemplified through the application of a basic snake game, where inputs and outputs are determined to enable the snake to stay alive.', "The process of saving a trained model in Keras is demonstrated, showcasing the simplicity of the process by using the 'model.save' function and specifying the desired name and extension for the saved model.", 'The chapter discusses the installation of TensorFlow 2.0, including potential issues with NumPy, and the installation of additional libraries like matplotlib and pandas.', 'The significance of hyper parameters and hyper tuning in neural networks and machine learning is highlighted, emphasizing the iterative process of changing individual parameters to achieve a better and more accurate model.', 'The chapter covers the preprocessing of text data to make it consistent for model input, defining the model architecture with specific layers and explaining their functions, emphasizing the importance of consistency in data formatting and length, and then proceeds to define the model architecture.', "The chapter discusses redefining training and testing data by trimming and normalizing it to 250 words using Kara's pre raw sassing sequence pad function, with parameters such as pad value and pad type, aiming to make all values equal.", 'The process of flattening data for neural network input is explained using a 28x28 pixel example to generate 784 pixels, enabling the configuration of a flattened input layer for the neural network.']}