{"id":5129,"date":"2026-08-10T09:19:51","date_gmt":"2026-08-10T09:19:51","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-deep-learning-with-python-second-edition-video-edition-2021-4\/"},"modified":"2026-08-10T09:19:51","modified_gmt":"2026-08-10T09:19:51","slug":"oreilly-deep-learning-with-python-second-edition-video-edition-2021-4","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-deep-learning-with-python-second-edition-video-edition-2021-4\/","title":{"rendered":"Oreilly \u2013 Deep Learning with Python, Second Edition, Video Edition 2021-4"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p data-sourcepos=\"4:1-4:38\"><span style=\"vertical-align: inherit\">Deep Learning with Python Second Edition Video Edition course. This video tutorial presents the book \u201cDeep Learning with Python, Second Edition\u201d in video and audio format. In this course, the speaker reads the book while the content, images, codes, diagrams and text are displayed on the screen. This course is like an audio book that you can also watch as a video.<\/span><\/p>\n<p data-sourcepos=\"7:1-7:39\"><span style=\"vertical-align: inherit\">This course will help you become familiar with the significant advances in deep learning. Written by the creator of the Keras library, this course teaches you practical deep learning techniques with Python that are easily applicable in the real world. In this course, complex concepts are explained in simple language and you will be taught through practical Python codes. Also, valuable insights into different model architectures and teaching tips are provided.<\/span><\/p>\n<h3 class=\"\" data-sourcepos=\"9:1-9:24\"><span style=\"vertical-align: inherit\">What you will learn<\/span><\/h3>\n<ul data-sourcepos=\"10:1-12:5\">\n<li data-sourcepos=\"10:1-10:28\"><span style=\"vertical-align: inherit\">Deep learning from the basics<\/span><\/li>\n<li data-sourcepos=\"11:1-11:28\"><span style=\"vertical-align: inherit\">Image classification and segmentation<\/span><\/li>\n<li data-sourcepos=\"12:1-12:5\"><span style=\"vertical-align: inherit\">Time series forecasting<\/span><\/li>\n<li data-sourcepos=\"13:1-13:30\"><span style=\"vertical-align: inherit\">Text classification and machine translation<\/span><\/li>\n<li data-sourcepos=\"14:1-15:0\"><span style=\"vertical-align: inherit\">Text production, neural style transfer and image production<\/span><\/li>\n<\/ul>\n<h3 class=\"\" data-sourcepos=\"16:1-16:30\"><span style=\"vertical-align: inherit\">This course is suitable for people who<\/span><\/h3>\n<ul data-sourcepos=\"17:1-18:58\">\n<li data-sourcepos=\"17:1-17:41\"><span style=\"vertical-align: inherit\">They are proficient in Python programming language.<\/span><\/li>\n<li data-sourcepos=\"18:1-18:58\"><span style=\"vertical-align: inherit\">No previous experience in Keras, TensorFlow or machine learning.<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Course details<\/span><\/h3>\n<ul>\n<li><span style=\"vertical-align: inherit\">Publisher: <\/span><a href=\"https:\/\/href.li\/?https:\/\/www.oreilly.com\/library\/view\/deep-learning-with\/9781617296864VE\/?_gl=1*19214ko*_ga*Mzg5OTcwOTAxLjE3MjM2NTg1ODA.*_ga_092EL089CH*MTcyMzgwODk4MC40LjEuMTcyMzgwOTMzNC42MC4wLjA.\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Author: <\/span><a href=\"https:\/\/downloadlynet.ir\/tag\/francois-chollet\/\"><span style=\"vertical-align: inherit\">Francois Chollet<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Training level: beginner to advanced<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Training duration: 15 hours and 2 minutes<\/span><\/li>\n<li><span style=\"vertical-align: inherit\">Number of courses: 96<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<ul>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 1 What is deep learning?<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 1 Learning rules and representations from data<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 1 Understanding how deep learning works, in three figures<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 1 Before deep learning: A brief history of machine learning<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 1 Back to neural networks<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 1 Why deep learning? Why now?<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 1 Algorithms<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 2 The mathematical building blocks of neural networks<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 2 Data representations for neural networks<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 2 Real-world examples of data tensors<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 2 The gears of neural networks: Tensor operations<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 2 Tensor reshaping<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 2 The engine of neural networks: Gradient-based optimization<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 2 Derivative of a tensor operation: The gradient<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 2 Chaining derivatives: The Backpropagation algorithm<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 2 Looking back at our first example<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 3 Introduction to Keras and TensorFlow<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 3 Setting up a deep learning workspace<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 3 First steps with TensorFlow<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 3 Anatomy of a neural network: Understanding core Keras APIs<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 3 The \u201ccompile\u201d step: Configuring the learning process<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 4 Getting started with neural networks: Classification and regression<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 4 Building your model<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 4 Classifying newswires: A multiclass classification example<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 4 Predicting house prices: A regression example<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 5 Fundamentals of machine learning<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 5 The nature of generalization in deep learning<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 5 Evaluating machine learning models<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 5 Improving model fit<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 5 Improving generalization<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 5 Regularizing your model<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 6 The universal workflow of machine learning<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 6 Collect a dataset<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 6 Develop a model<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 6 Beat a baseline<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 6 Deploy the model<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 6 Monitor your model in the wild<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 7 Working with Keras: A deep dive<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 7 Subclassing the Model class<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 7 Using built-in training and evaluation loops<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 7 Writing your own training and evaluation loops<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 7 Make it fast with tf.function<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 8 Introduction to deep learning for computer vision<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 8 The convolution operation<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 8 Training a convnet from scratch on a small dataset<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 8 Data preprocessing<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 8 Leveraging a pretrained model<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 8 Feature extraction with a pretrained model<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 9 Advanced deep learning for computer vision<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 9 Modern convnet architecture patterns<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 9 Residual connections<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 9 Depthwise separable convolutions<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 9 Interpreting what convnets learn<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 9 Visualizing convnet filters<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 9 Visualizing heatmaps of class activation<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 10 Deep learning for timeseries<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 10 Preparing the data<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 10 Let\u2019s try a basic machine learning model<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 10 Understanding recurrent neural networks<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 10 A recurrent layer in Keras<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 10 Advanced use of recurrent neural networks<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 10 Using bidirectional RNNs<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 Deep learning for text<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 Preparing text data<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 Vocabulary indexing<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 Two approaches for representing groups of words: Sets and sequences<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 Processing words as a sequence: The sequence model approach, Part 1<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 Processing words as a sequence: The sequence model approach, Part 2<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 The Transformer architecture<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 The Transformer encoder<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 Beyond text classification: Sequence-to-sequence learning<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 11 Sequence-to-sequence learning with Transformer<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 12 Generative deep learning<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 12 How do you generate sequence data?<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 12 A text-generation callback with variable-temperature sampling<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 12 DeepDream<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 12 Neural style transfer<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 12 Generating images with variational autoencoders<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 12 Implementing a VAE with Keras<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 12 A bag of tricks<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 13 Best practices for the real world<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 13 Hyperparameter optimization<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 13 Scaling-up model training<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 13 Multi-GPU training<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 13 TPU training<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 Conclusions<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 Key enabling technologies<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 Key network architectures<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 The limitations of deep learning<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 Local generalization vs. extreme generalization<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 The purpose of intelligence<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 Setting the course toward greater generality in AI<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 Implementing intelligence: The missing ingredients<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 The missing half of the picture<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 Blending together deep learning and program synthesis<\/li>\n<li class=\"toc-level-1 t-toc-level-1\" dir=\"ltr\" style=\"text-align: left\">Chapter 14 Lifelong learning and modular subroutine reuse<\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Images of Deep Learning with Python Second Edition Video Edition course<\/span><\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-921997 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2024\/08\/Deep-Learning-with-Python-Second-Edition-Video-Edition-1.jpg\" alt=\"Deep Learning with Python Second Edition Video Edition\" width=\"1163\" height=\"421\"><\/p>\n<h3><span style=\"vertical-align: inherit\">Sample video of the course<\/span><\/h3>\n<div style=\"width: 640px;\" class=\"wp-video\"><span class=\"mejs-offscreen\">Video Player<\/span><\/p>\n<div id=\"mep_0\" class=\"mejs-container mejs-container-keyboard-inactive wp-video-shortcode mejs-video\" tabindex=\"0\" role=\"application\" aria-label=\"Video Player\" style=\"width: 640px; height: 360px; min-width: 217px;\">\n<div class=\"mejs-inner\">\n<div class=\"mejs-mediaelement\"><mediaelementwrapper id=\"video-135925-1\"><video class=\"wp-video-shortcode\" 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Up\/Down Arrow keys to increase or decrease volume.<\/span><\/p>\n<div class=\"mejs-volume-total\">\n<div class=\"mejs-volume-current\" style=\"bottom: 0px; height: 100%;\"><\/div>\n<div class=\"mejs-volume-handle\" style=\"bottom: 100%; margin-bottom: -3px;\"><\/div>\n<\/div>\n<p><\/a><\/div>\n<div class=\"mejs-button mejs-fullscreen-button\"><button type=\"button\" aria-controls=\"mep_0\" title=\"Fullscreen\" aria-label=\"Fullscreen\" tabindex=\"0\"><\/button><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<div>\n<h3><span style=\"vertical-align: inherit\">Installation guide<\/span><\/h3>\n<p><span style=\"vertical-align: inherit\">After Extract, view with your favorite Player.<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Subtitle: None<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">Quality: 720p<\/span><\/p>\n<\/div>\n<h3><span style=\"vertical-align: inherit\">download link<\/span><\/h3>\n<p><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Oreilly_Deep_Learning_with_Python_Second_Edition_Video_Edition_2021-4.part1_Downloadly.ir.rar?nocache=1786103435\"><span style=\"vertical-align: inherit\">Download part 1 \u2013 1 GB<\/span><\/a><\/p>\n<p><a href=\"https:\/\/dl3.downloadly.ir\/Files\/Elearning\/Oreilly_Deep_Learning_with_Python_Second_Edition_Video_Edition_2021-4.part2_Downloadly.ir.rar?nocache=1786103435\"><span style=\"vertical-align: inherit\">Download part 2 \u2013 546 MB<\/span><\/a><\/p>\n<p><span style=\"vertical-align: inherit\">File(s) password: www.downloadly.ir<\/span><span style=\"vertical-align: inherit\">File size<\/span><\/p>\n<p><span style=\"vertical-align: inherit\">1.5 GB<\/span><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"<p>Description Deep Learning with Python Second Edition Video Edition course. 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