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Key benefits

Understand how to use PyTorch 1.x to build advanced neural network models

Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques

Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more

Description

Deep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.

What you will learn

Implement text and music generating models using PyTorch

Build a deep Q-network (DQN) model in PyTorch

Export universal PyTorch models using Open Neural Network Exchange (ONNX)

Become well-versed with rapid prototyping using PyTorch with fast.ai

Perform neural architecture search effectively using AutoML

Easily interpret machine learning (ML) models written in PyTorch using Captum

Design ResNets, LSTMs, Transformers, and more using PyTorch

Find out how to use PyTorch for distributed training using the torch.distributed API

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

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shashank sagar jha Feb 22, 2021

Best book.. Superb clarity.. Best part is the examples provided in this book. Almost all important topics are covered in depth.

Amazon Verified review

deepak Feb 23, 2021

I did not work with PyTorch before and although the book states the basic knowledge of PyTorch as a pre-requisite, I was still able to learn from scratch thanks to the easy progression of topics. The book does cover a lot of ground in my opinion ranging from model architectures, to applications such as music generation and to model deployment and other such engineering considerations. The second half of the book helps get a feel of how deep learning works in practice in real-world applications. I especially liked the inclusion of a jupyter notebook based exercise in each and every chapter. Definitely worth a read.

Shekhar Feb 23, 2021

Ashish has done a brilliant job with the book in explaining how to use PyTorch to build advanced neural network models. Thanks for sharing the precious knowledge with everyone.

saurabh Mar 08, 2021

It was really nice to see all the requirements clearly mentioned in the introductory chapter to get started with the book. No need to hit your head against the wall searching for all the libraries and packages on the internet. You could fire your laptop and get going.Book focuses on practical approach to learning rather than shoving theories down one’s throat. The language used is simple, clear and to the point.I haven’t finished the book yet but it has been a great learning experience so far. A must have book for those learning NN by themselves.

Surendra Kumar Feb 14, 2021

I recommend this book to anyone looking for up to date Pytroch/Deep Learning material.It is very well written, goes into enough math details. The author doesn't scrimp on either introduction concepts or in supporting code. He spends over 450 pages to get it all spelled out carefully in text, pictures , and graphs that should satisfy the most severe critics.

Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

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