PyTorch Deep Learning Hands-On: Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily - Softcover
Synopsis
Developing image analysis apps, GAN-based networks, reinforcement learning algorithms and text engineering routines with Deep Learning PyTorch applications
Key Features
- The first book-length introduction to PyTorch
- Covers the whole range of possible applications that can be written on PyTorch
- Focuses on the APIs, and treats algorithms as secondary
Book Description
Deep Learning is probably the fastest-growing, but also the most complex area of applied computing today. There are two major frameworks dominating the Deep Learning API landscape - Google's TensorFlow, and Facebook's PyTorch. Deriving from the open source Torch framework written in Lua, it was under the leadership of AI guru Yann LeCun that Pytorch developed into a major alternative.
PyTorch uses autodifferentiation to make it possible for developers to introduce new behaviors into their neural networks, without having to restart their networks. This is possibly the most important innovation for major machine and deep learning frameworks implemented in Pytorch. Also, PyTorch threads can run on CPUs as well as GPUs, providing major efficiency gains in the process.
This book shows us how to make the simplicity and power of Pytorch work for a Python developer. The first application we learn about is how how to process images using CNNs, but new algorithms like GANs and and natural language processing algorithms are introduced as well. The book ends with a chapter on reinforcement learning and how put PyTorch application into production
What you will learn
- Processing, improving and recognizing image features
- Finding, interpreting and deriving insights from unstructured textual data
- Learning several varieties of General Adversarial Networks (GANs)
- Apply PyTorch implementations of reinforcement learning algorithms
- Put PyTorch projects through a production cycle
Who This Book Is For
Fluency in Python is assumed. Basic deep learning approaches should be familiar to the reader. This book is meant to be an introduction to PyTorch, and tries to show the breadth of applications PyTorch can be put to.
"synopsis" may belong to another edition of this title.
About the Author
After doing his computer science degree in Kerala, Sherin Thomas became a developer and AI expert for various Indian companies. A strong interest in open source AI led Sherin to start writing and presenting at conferences, culminating in this book on PyTorch.
"About this title" may belong to another edition of this title.
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Paperback. Condition: New. Hands-on projects cover all the key deep learning methods built step-by-step in PyTorchKey FeaturesInternals and principles of PyTorchImplement key deep learning methods in PyTorch: 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 build: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. Seller Inventory # LU-9781788834131
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