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Deep Learning with PyTorch Quick Start Guide
Deep Learning with PyTorch Quick Start Guide

Deep Learning with PyTorch Quick Start Guide: Learn to train and deploy neural network models in Python

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Deep Learning with PyTorch Quick Start Guide

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Key benefits

  • Clear and concise explanations
  • Gives important insights into deep learning models
  • Practical demonstration of key concepts

Description

PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power. This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders. You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text. By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.

Who is this book for?

Developers and Data Scientist familiar with Machine Learning but new to deep learning, or existing practitioners of deep learning who would like to use PyTorch to train their deep learning models will find this book to be useful. Having knowledge of Python programming will be an added advantage, while previous exposure to PyTorch is not needed.

What you will learn

  • Set up the deep learning environment using the PyTorch library
  • Learn to build a deep learning model for image classification
  • Use a convolutional neural network for transfer learning
  • Understand to use PyTorch for natural language processing
  • Use a recurrent neural network to classify text
  • Understand how to optimize PyTorch in multiprocessor and distributed environments
  • Train, optimize, and deploy your neural networks for maximum accuracy and performance
  • Learn to deploy production-ready models

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 24, 2018
Length: 158 pages
Edition : 1st
Language : English
ISBN-13 : 9781789539738
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What do you get with eBook?

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Product Details

Publication date : Dec 24, 2018
Length: 158 pages
Edition : 1st
Language : English
ISBN-13 : 9781789539738
Category :
Languages :
Concepts :
Tools :

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Frequently bought together


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Table of Contents

6 Chapters
Introduction to PyTorch Chevron down icon Chevron up icon
Deep Learning Fundamentals Chevron down icon Chevron up icon
Computational Graphs and Linear Models Chevron down icon Chevron up icon
Convolutional Networks Chevron down icon Chevron up icon
Other NN Architectures Chevron down icon Chevron up icon
Getting the Most out of PyTorch Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.3
(3 Ratings)
5 star 0%
4 star 66.7%
3 star 0%
2 star 33.3%
1 star 0%
Benny Prijono Aug 08, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This book is compact (only 158 printed pages) but yet it’s rather comprehensive introductory book to PyTorch. The way it lays out the progression of the projects is quite nice. It starts out with logistic regression, linear regression, multi-class logistic regression, and then NN, NN with some hidden layers, then CNN, and then RNN and LSTM etc. For each topic the discussion starts with some theories and then the explanation of the parts of the code. The theory part can be very thin though (for example only 1-2 pages for RNN!), so I won't try to understand the theories from this book.This book is very good when treated as introductory book to PyTorch. I would give it five stars, but unfortunately the RNN chapter is quite hard to understand.
Amazon Verified review Amazon
杉原 優 Jan 25, 2019
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Although too many typos inside of this book, this book is very helpful for beginners.
Amazon Verified review Amazon
Tae S. Shin Feb 06, 2019
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
With Kindle version, example code is in figures and its font size is so small and can't be controlled. Just returned for refund.
Amazon Verified review Amazon
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