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PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch
Key FeaturesInternals and principles of PyTorchImplement key deep learning methods in CNNs, GANs, RNNs, reinforcement learning, and moreBuild deep learning workflows and take deep learning models from prototyping to productionBook DescriptionPyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly.
PyTorch Deep Learning Hands-On shows how to implement the major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools.
Each chapter focuses on a different area of deep learning. Chapters start with a refresher on how the model works, before sharing the code you need to implement them in PyTorch.
This book is ideal if you want to rapidly add PyTorch to your deep learning toolset.
What you will learnUse PyTorch to
Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and moreConvolutional Neural Networks – create advanced computer vision systemsRecurrent Neural Networks – work with sequential data such as natural language and audioGenerative Adversarial Networks – create new content with models including SimpleGAN and CycleGANReinforcement Learning – develop systems that can solve complex problems such as driving or game playingDeep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packagesProduction-ready models – package your models for high-performance production environmentsWho this book is forMachine learning engineers who want to put PyTorch to work.
Table of ContentsDeep Learning Walkthrough and PyTorch IntroductionA Simple Neural NetworkDeep Learning WorkflowComputer VisionSequential Data ProcessingGenerative NetworksReinforcement LearningPyTorch to Production
Key FeaturesInternals and principles of PyTorchImplement key deep learning methods in CNNs, GANs, RNNs, reinforcement learning, and moreBuild deep learning workflows and take deep learning models from prototyping to productionBook DescriptionPyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch. It is not an academic textbook and does not try to teach deep learning principles. The book will help you most if you want to get your hands dirty and put PyTorch to work quickly.
PyTorch Deep Learning Hands-On shows how to implement the major deep learning architectures in PyTorch. It covers neural networks, computer vision, CNNs, natural language processing (RNN), GANs, and reinforcement learning. You will also build deep learning workflows with the PyTorch framework, migrate models built in Python to highly efficient TorchScript, and deploy to production using the most sophisticated available tools.
Each chapter focuses on a different area of deep learning. Chapters start with a refresher on how the model works, before sharing the code you need to implement them in PyTorch.
This book is ideal if you want to rapidly add PyTorch to your deep learning toolset.
What you will learnUse PyTorch to
Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and moreConvolutional Neural Networks – create advanced computer vision systemsRecurrent Neural Networks – work with sequential data such as natural language and audioGenerative Adversarial Networks – create new content with models including SimpleGAN and CycleGANReinforcement Learning – develop systems that can solve complex problems such as driving or game playingDeep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packagesProduction-ready models – package your models for high-performance production environmentsWho this book is forMachine learning engineers who want to put PyTorch to work.
Table of ContentsDeep Learning Walkthrough and PyTorch IntroductionA Simple Neural NetworkDeep Learning WorkflowComputer VisionSequential Data ProcessingGenerative NetworksReinforcement LearningPyTorch to Production
252 pages, Kindle Edition
Published April 30, 2019
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8 people want to read
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Displaying 1 of 1 review
June 1, 2019
After I already picked a copy of the book I suddenly realized it is only 200 pages plus so what would I learn?
Quite a bit. Despite being relatively succinct at first glance the author managed to actually squeeze in material beyond the basics. Not to server as a spoiler, this book has helped me to advance on my own terms without spending too much time in the book. The learning process basically starts right away, thus the Hands-On in the title.
Good points: most of the tools are the common ones, Python, Ubunty. With a minor exception being Flask that I really was never exposed to. But frankly I find it not intimidating as some other Web Frameworks I prefer not to mention. One of very few books that explain things visually as Neural Network kinds. Overall a good coverage of the most typically found in use NNs.
What I found a bit difficult to understand - formulas.
What could be improved: code annotations are too sparse (not everywhere).
Still it is a 5/5 for me.
Quite a bit. Despite being relatively succinct at first glance the author managed to actually squeeze in material beyond the basics. Not to server as a spoiler, this book has helped me to advance on my own terms without spending too much time in the book. The learning process basically starts right away, thus the Hands-On in the title.
Good points: most of the tools are the common ones, Python, Ubunty. With a minor exception being Flask that I really was never exposed to. But frankly I find it not intimidating as some other Web Frameworks I prefer not to mention. One of very few books that explain things visually as Neural Network kinds. Overall a good coverage of the most typically found in use NNs.
What I found a bit difficult to understand - formulas.
What could be improved: code annotations are too sparse (not everywhere).
Still it is a 5/5 for me.
Displaying 1 of 1 review