Deep Learning with PyTorch 1.x: Implement deep learning techniques and neural network architecture variants using Python

Book Description
PyTorch is gaining the attention of deep learning researchers and data science professionals due to its accessibility and efficiency, along with the fact that it’s more native to the Python way of development. This book will get you up and running with this cutting-edge deep learning library, effectively guiding you through implementing deep learning concepts.

In this second edition, you’ll learn the fundamental aspects that power modern deep learning, and explore the new features of the PyTorch 1.x library. You’ll understand how to solve real-world problems using CNNs, RNNs, and LSTMs, along with discovering state-of-the-art modern deep learning architectures, such as ResNet, DenseNet, and Inception. You’ll then focus on applying neural networks to domains such as computer vision and NLP. Later chapters will demonstrate how to build, train, and scale a model with PyTorch and also cover complex neural networks such as GANs and autoencoders for producing text and images. In addition to this, you’ll explore GPU computing and how it can be used to perform heavy computations. Finally, you’ll learn how to work with deep learning-based architectures for transfer learning and reinforcement learning problems.

By the end of this book, you’ll be able to confidently and easily implement deep learning applications in PyTorch.

What you will learn
Build text classification and language modeling systems using neural networks
Implement transfer learning using advanced CNN architectures
Use deep reinforcement learning techniques to solve optimization problems in PyTorch
Mix multiple models for a powerful ensemble model
Build image classifiers by implementing CNN architectures using PyTorch
Get up to speed with reinforcement learning, GANs, LSTMs, and RNNs with real-world examples
Who this book is for
This book is for data scientists and machine learning engineers looking to work with deep learning algorithms using PyTorch 1.x. You will also find this book useful if you want to migrate to PyTorch 1.x. Working knowledge of Python programming and some understanding of machine learning will be helpful.

Table of Contents
Getting Started with Deep Learning Using PyTorch
Building Blocks of Neural Networks
Diving Deep into Neural Networks
Deep Learning for Computer Vision
Natural Language Processing with Sequence data
Implementing Autoencoders
Working with Generative Adversarial Networks
Transfer Learning with Modern Network Architectures
Deep Reinforcement Learning
What Next?