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Book cover for PyTorch Deep Learning Hands-On, a book by Sherin Thomas, Sudhanshu Passi Book cover for PyTorch Deep Learning Hands-On, a book by Sherin Thomas, Sudhanshu Passi

PyTorch Deep Learning Hands-On

Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
2019 ᛫


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Summary


Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch


Key Features


  • Internals and principles of PyTorch

  • Implement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and more

  • Build deep learning workflows and take deep learning models from prototyping to production

Book Description


PyTorch 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 learn


Use PyTorch to build:



  • Simple Neural Networks – build neural networks the PyTorch way, with high-level functions, optimizers, and more

  • Convolutional Neural Networks – create advanced computer vision systems

  • Recurrent Neural Networks – work with sequential data such as natural language and audio

  • Generative Adversarial Networks – create new content with models including SimpleGAN and CycleGAN

  • Reinforcement Learning – develop systems that can solve complex problems such as driving or game playing

  • Deep Learning workflows – move effectively from ideation to production with proper deep learning workflow using PyTorch and its utility packages

  • Production-ready models – package your models for high-performance production environments

Who this book is for


Machine learning engineers who want to put PyTorch to work.