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Deep Learning with PyTorch
Deep Learning with PyTorch

Deep Learning with PyTorch: A practical approach to building neural network models using PyTorch

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Profile Icon Vishnu Subramanian
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€18.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4 (13 Ratings)
Paperback Feb 2018 262 pages 1st Edition
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€8.99 €26.99
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€32.99
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Arrow left icon
Profile Icon Vishnu Subramanian
Arrow right icon
€18.99 per month
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4 (13 Ratings)
Paperback Feb 2018 262 pages 1st Edition
eBook
€8.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m
eBook
€8.99 €26.99
Paperback
€32.99
Subscription
Free Trial
Renews at €18.99p/m

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Deep Learning with PyTorch

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Key benefits

  • Learn PyTorch for implementing cutting-edge deep learning algorithms.
  • Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios;
  • Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples;

Description

Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, TensorFlow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics. This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images. By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease.

Who is this book for?

This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.

What you will learn

  • • Use PyTorch for GPU-accelerated tensor computations
  • • Build custom datasets and data loaders for images and test the models using torchvision and torchtext
  • • Build an image classifier by implementing CNN architectures using PyTorch
  • • Build systems that do text classification and language modeling using RNN, LSTM, and GRU
  • • Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning
  • • Learn how to mix multiple models for a powerful ensemble model
  • • Generate new images using GAN's and generate artistic images using style transfer

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 23, 2018
Length: 262 pages
Edition : 1st
Language : English
ISBN-13 : 9781788624336
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Product Details

Publication date : Feb 23, 2018
Length: 262 pages
Edition : 1st
Language : English
ISBN-13 : 9781788624336
Category :
Languages :
Concepts :
Tools :

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Table of Contents

10 Chapters
Getting Started with Deep Learning Using PyTorch Chevron down icon Chevron up icon
Building Blocks of Neural Networks Chevron down icon Chevron up icon
Diving Deep into Neural Networks Chevron down icon Chevron up icon
Fundamentals of Machine Learning Chevron down icon Chevron up icon
Deep Learning for Computer Vision Chevron down icon Chevron up icon
Deep Learning with Sequence Data and Text Chevron down icon Chevron up icon
Generative Networks Chevron down icon Chevron up icon
Modern Network Architectures Chevron down icon Chevron up icon
What Next? Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4
(13 Ratings)
5 star 38.5%
4 star 23.1%
3 star 0%
2 star 15.4%
1 star 23.1%
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P. Romero Jun 11, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you want in-depth learning on PyTorch, look no further. The author succeeded in presenting practical knowledge on PyTorch that the reader can easily put to use. Recommended.
Amazon Verified review Amazon
J K Mar 04, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A very good book for startersHands on training examples are good.The book is must have for deep learning enthusiasts
Amazon Verified review Amazon
archit gupta Nov 01, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Nice book
Amazon Verified review Amazon
Sumesh Oct 21, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good book for beginners.
Amazon Verified review Amazon
krishna c. Sep 02, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very good book
Amazon Verified review Amazon
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