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Deep Learning with PyTorch

By : Vishnu Subramanian

3.4 (13)

Buy this Book

Deep Learning with PyTorch

3.4 (13)

By: Vishnu Subramanian

Buy this Book

Overview of this book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Getting Started with Deep Learning Using PyTorch

Artificial intelligence

Machine learning

Deep learning

Summary

Building Blocks of Neural Networks

Installing PyTorch

Our first neural network

Summary

Diving Deep into Neural Networks

Deep dive into the building blocks of neural networks

Summary

Fundamentals of Machine Learning

Three kinds of machine learning problems

Machine learning glossary

Evaluating machine learning models

Data preprocessing and feature engineering

Overfitting and underfitting

Workflow of a machine learning project

Summary

Deep Learning for Computer Vision

Introduction to neural networks

Building a CNN model from scratch

Creating and exploring a VGG16 model

Calculating pre-convoluted features

Understanding what a CNN model learns

Visualizing weights of the CNN layer

Summary

Deep Learning with Sequence Data and Text

Working with text data

Training word embedding by building a sentiment classifier

Using pretrained word embeddings

Recursive neural networks

LSTM

Convolutional network on sequence data

Summary

Generative Networks

Neural style transfer

Generative adversarial networks

Deep convolutional GAN

Language modeling

Summary

Modern Network Architectures

Modern network architectures

Densely connected convolutional networks – DenseNet

Model ensembling

Encoder-decoder architecture

Summary

What Next?

What next?

Overview

Interesting ideas to explore

How to keep yourself updated

Summary

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Getting Started with Deep Learning Using PyTorch

Deep learning (DL) has revolutionized industry after industry. It was once famously described by Andrew Ng on Twitter:

Artificial Intelligence is the new electricity!

Electricity transformed countless industries; artificial intelligence (AI) will now do the same.

AI and DL are used like synonyms, but there are substantial differences between the two. Let's demystify the terminology used in the industry so that you, as a practitioner, will be able to differentiate between signal and noise.

In this chapter, we will cover the following different parts of AI: