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Mastering PyTorch
Mastering PyTorch

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

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Profile Icon Ashish Ranjan Jha
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$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (43 Ratings)
Paperback Feb 2021 450 pages 1st Edition
eBook
$47.99
Paperback
$59.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon Ashish Ranjan Jha
Arrow right icon
$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (43 Ratings)
Paperback Feb 2021 450 pages 1st Edition
eBook
$47.99
Paperback
$59.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$47.99
Paperback
$59.99
Subscription
Free Trial
Renews at $12.99p/m

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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.

Who is this book for?

This book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.

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

Product Details

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Publication date : Feb 12, 2021
Length: 450 pages
Edition : 1st
Language : English
ISBN-13 : 9781789614381
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Product Details

Publication date : Feb 12, 2021
Length: 450 pages
Edition : 1st
Language : English
ISBN-13 : 9781789614381
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(43 Ratings)
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4 star 4.7%
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1 star 4.7%
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shashank sagar jha Feb 22, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Best book.. Superb clarity.. Best part is the examples provided in this book. Almost all important topics are covered in depth.
Amazon Verified review Amazon
deepak Feb 23, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Shekhar Feb 23, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
saurabh Mar 08, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
Surendra Kumar Feb 14, 2021
Full star icon Full star icon Full star icon Full star icon Full star icon 5
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.
Amazon Verified review Amazon
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