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Books > Computing & IT > Applications of computing > Artificial intelligence > Natural language & machine translation
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PyTorch Deep Learning Hands-On - Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily (Paperback)
Loot Price: R1,094
Discovery Miles 10 940
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PyTorch Deep Learning Hands-On - Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily (Paperback)
Expected to ship within 18 - 22 working days
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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 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 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 forMachine learning
engineers who want to put PyTorch to work.
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