{"id":8510,"date":"2026-08-10T09:56:51","date_gmt":"2026-08-10T09:56:51","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-deep-learning-with-pytorch-video-edition-2021-7\/"},"modified":"2026-08-10T09:56:51","modified_gmt":"2026-08-10T09:56:51","slug":"oreilly-deep-learning-with-pytorch-video-edition-2021-7","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-deep-learning-with-pytorch-video-edition-2021-7\/","title":{"rendered":"Oreilly \u2013 Deep Learning with PyTorch video edition 2021-7"},"content":{"rendered":"<div class=\"w-post-elm post_content\">\n<h2><span style=\"vertical-align: inherit\">Description<\/span><\/h2>\n<p data-sourcepos=\"5:1-5:917\"><span style=\"vertical-align: inherit\">Deep Learning with PyTorch video edition. This course teaches you how to build neural networks and deep learning systems using the PyTorch library. This hands-on book quickly walks you through building a real-world example from scratch: a tumor image classifier. Along the way, it covers best practices for the entire deep learning pipeline, including the PyTorch tensor API, loading data into Python, supervising training, and visualizing results. After covering the basics, the book takes you on a journey through larger projects. The main focus of the book is a neural network designed to detect cancer. You will explore methods for training networks with limited inputs and begin processing data to obtain some results. You will examine unreliable initial results and focus on how to detect and fix problems in your neural network. Finally, you will explore ways to improve your results by training with augmented data, making improvements to the model architecture, and performing other fine-tuning.<\/span><\/p>\n<h3 data-sourcepos=\"7:1-7:25\"><strong><span style=\"vertical-align: inherit\">What you will learn:<\/span><\/strong><\/h3>\n<ul data-sourcepos=\"9:1-13:0\">\n<li data-sourcepos=\"9:1-9:26\"><span style=\"vertical-align: inherit\">Training deep neural networks<\/span><\/li>\n<li data-sourcepos=\"10:1-10:34\"><span style=\"vertical-align: inherit\">Implementing loss modules and functions<\/span><\/li>\n<li data-sourcepos=\"11:1-11:53\"><span style=\"vertical-align: inherit\">Using pre-trained models from PyTorch Hub<\/span><\/li>\n<li data-sourcepos=\"12:1-13:0\"><span style=\"vertical-align: inherit\">Review code samples in Jupyter Notebooks<\/span><\/li>\n<\/ul>\n<h3 data-sourcepos=\"14:1-14:31\"><strong><span style=\"vertical-align: inherit\">This course is suitable for people who:<\/span><\/strong><\/h3>\n<ul data-sourcepos=\"16:1-16:60\">\n<li data-sourcepos=\"16:1-16:60\"><span style=\"vertical-align: inherit\">Python programmers are passionate about machine learning.<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Course details: Deep Learning with PyTorch video edition<\/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\/9781617295263VE\/\" target=\"_blank\" rel=\"noopener\"><span style=\"vertical-align: inherit\">Oreilly<\/span><\/a><\/li>\n<li><span style=\"vertical-align: inherit\">Instructor: <\/span><a href=\"https:\/\/downloadlynet.ir\/tag\/eli-stevens\/\"><span style=\"vertical-align: inherit\">Eli Stevens<\/span><\/a><span style=\"vertical-align: inherit\"> , <a href=\"https:\/\/downloadlynet.ir\/tag\/luca-antiga\/\">Luca Antiga<\/a>, <a href=\"https:\/\/downloadlynet.ir\/tag\/thomas-viehmann\/\">Thomas Viehmann<\/a><\/span><\/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 32 minutes<\/span><\/li>\n<\/ul>\n<h3><span style=\"vertical-align: inherit\">Course headings<\/span><\/h3>\n<div class=\"ud-block-list-item ud-block-list-item-small ud-block-list-item-tight ud-block-list-item-neutral ud-text-sm\">\n<ul>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 1. Core PyTorch<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Introducing deep learning and the PyTorch Library<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Why PyTorch?<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. An overview of how PyTorch supports deep learning projects<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 1. Hardware and software requirements<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Pretrained networks<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Obtaining a pretrained network for image recognition<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. Ready, set, almost run<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. A pretrained model that fakes it until it makes it<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. A network that turns horses into zebras<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 2. A pretrained network that describes scenes<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. It starts with a tensor<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. Indexing tensors<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 3. The tensor API<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 3. Tensor metadata: Size, offset, and stride<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 3. NumPy interoperability<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 4. Real-world data representation using tensors<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 4. 3D images: Volumetric data<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 4. Representing scores<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 4. Working with time series<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 4. Ready for training<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 4. One-hot encoding whole words<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 4. Text embeddings as a blueprint<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 5. The mechanics of learning<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 5. Gathering some data<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 5. Down along the gradient<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 5. Normalizing inputs<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 5. Optimizers a la carte<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 5. Generalizing to the validation set<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 6. Using a neural network to fit the data<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 6. More activation functions<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 6. The PyTorch nn module<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 6. Finally a neural network<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 7. Telling birds from airplanes: Learning from images<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 7. Distinguishing birds from airplanes<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 7. Representing the output as probabilities<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 7. Training the classifier<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 7. The limits of going fully connected<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 8. Using convolutions to generalize<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 8. Convolutions in action<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 8. Looking further with depth and pooling<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 8. Subclassing nn.Module<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 8. Training our convnet<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 8. Helping our model to converge and generalize: Regularization<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 8. Going deeper to learn more complex structures: Depth<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 8. Comparing the designs from this section<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Part 2. Learning from images in the real world: Early detection of lung cancer<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 9. Using PyTorch to fight cancer<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 9. What is a CT scan, exactly?<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 9. In more detail, we will do the following<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 9. Why can\u2019t we just throw data at a neural network until it works?<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 9. What is a nodule?<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 10. Combining data sources into a unified dataset<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 10. Training and validation sets<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 10. Loading individual CT scans<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 10. Locating a nodule using the patient coordinate system<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 10. A straightforward dataset implementation<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 10. Constructing our dataset in LunaDataset.__init__<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 11. Training a classification model to detect suspected tumors<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 11. Pretraining setup and initialization<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 11. Our first-pass neural network design<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 11. The full model<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 11. Outputting performance metrics<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 11. Needed data for training<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 11. Running TensorBoard<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 11. Why isn\u2019t the model learning to detect nodules?<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 12. Improving training with metrics and augmentation<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 12. Graphing the positives and negatives<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 12. Our ultimate performance metric: The F1 score<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 12. What does an ideal dataset look like?<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 12. Samplers can reshape datasets<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 12. Revisiting the problem of overfitting<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 12. Seeing the improvement from data augmentation<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 13. Using segmentation to find suspected nodules<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 13. Semantic segmentation: Per-pixel classification<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 13. Updating the model for segmentation<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 13. Updating the dataset for segmentation<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 13. Building the ground truth data<\/li>\n<li dir=\"ltr\" style=\"text-align: left\">Chapter 13. Implementing Luna2dSegmentationDataset<\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 13. Designing our training and validation data<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 13. Updating the training script for segmentation<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 13. Getting images into TensorBoard<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 13. Results<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. End-to-end nodule analysis, and where to go next<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. Bridging CT segmentation and nodule candidate classification<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. Did we find a nodule? Classification to reduce false positives<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. Quantitative validation<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. Reusing preexisting weights: Fine-tuning<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. What we see when we diagnose<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. Beyond a single best model: Ensembling<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 14. Conclusion<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Part 3. Deployment<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Deploying to production<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Request batching<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Exporting models<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Interacting with the PyTorch JIT<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. TorchScript<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. LibTorch: PyTorch in C++<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. C++ from the beginning: The C++ API<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Going mobile<\/span><\/li>\n<li dir=\"ltr\" style=\"text-align: left\"><span style=\"vertical-align: inherit\">Chapter 15. Improving efficiency: Model design and quantization<\/span><\/li>\n<\/ul>\n<\/div>\n<h3><span style=\"vertical-align: inherit\">Course images<\/span><\/h3>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-961817 size-full\" src=\"https:\/\/downloadly.ir\/wp-content\/uploads\/2025\/02\/Deep-Learning-with-PyTorch-video-edition2.png\" alt=\"Deep Learning with PyTorch video edition\" width=\"992\" height=\"375\"><\/p>\n<h3><span style=\"vertical-align: inherit\">Sample course video<\/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-153865-1\"><video class=\"wp-video-shortcode\" id=\"video-153865-1_html5\" width=\"640\" height=\"360\" preload=\"metadata\" src=\"https:\/\/dl.downloadly.ir\/Files\/Elearning\/Sample\/Deep_Learning_with_PyTorch_video_edition_Downloadly.ir.mp4?_=1\" style=\"width: 640px; 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This course teaches you how to build neural networks and deep learning systems using the PyTorch library. <\/p>\n","protected":false},"author":1,"template":"","dgi_category":[10458],"dgi_tag":[75128,75129,75130,75131,75132,75133,75134,75135],"class_list":["post-8510","digital_item","type-digital_item","status-publish","has-post-thumbnail","hentry","dgi_category-video-tutorials","dgi_tag-course-deep-learning-with-pytorch-video-edition","dgi_tag-download-course-deep-learning-with-pytorch-video-edition","dgi_tag-download-deep-learning-with-pytorch-video-edition","dgi_tag-eli-stevens","dgi_tag-free-deep-learning-with-pytorch-video-edition","dgi_tag-free-download-deep-learning-with-pytorch-video-edition","dgi_tag-luca-antiga","dgi_tag-thomas-viehmann"],"_links":{"self":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8510","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item"}],"about":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/types\/digital_item"}],"author":[{"embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"version-history":[{"count":0,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/digital_item\/8510\/revisions"}],"wp:attachment":[{"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/media?parent=8510"}],"wp:term":[{"taxonomy":"dgi_category","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_category?post=8510"},{"taxonomy":"dgi_tag","embeddable":true,"href":"https:\/\/nokobox.com\/index.php\/wp-json\/wp\/v2\/dgi_tag?post=8510"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}