Book Image

Mastering PyTorch

By : Ashish Ranjan Jha
Book Image

Mastering PyTorch

By: Ashish Ranjan Jha

Overview of this book

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.
Table of Contents (20 chapters)
1
Section 1: PyTorch Overview
4
Section 2: Working with Advanced Neural Network Architectures
8
Section 3: Generative Models and Deep Reinforcement Learning
13
Section 4: PyTorch in Production Systems

Understanding how to transfer style between images

In Chapter 3, Deep CNN Architectures, we discussed convolutional neural networks (CNNs) in detail. CNNs are largely the most successful class of models when working with image data. We have seen how CNN-based architectures are the best-performing architectures of neural networks on tasks such as image classification, object detection, and so on. One of the core reasons behind this success is the ability of convolutional layers to learn spatial representations.

For example, in a dog versus cat classifier, the CNN model is essentially able to capture the content of an image in its higher-level features, which helps it detect dog-specific features against cat-specific features. We will leverage this ability of an image classifier CNN to grasp the content of an image.

We know that VGG is a powerful image classification model, as discussed in Chapter 3, Deep CNN Architectures. We are going to use the convolutional part of the VGG...