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

Summary

In this chapter, we explored generative models using PyTorch. Beginning with text generation, we utilized the transformer-based language model we built in the previous chapter to develop a text generator. We demonstrated how PyTorch can be used to convert a model that's been trained without supervision (a language model, in this case) into a data generator. After that, we exploited the pre-trained advanced transformer models that are available under the transformers library and used them as text generators. We discussed various text generation strategies, such as greedy search, beam search, and top-k and top-p sampling.

Next, we built an AI music composer from scratch. Using Mozart's piano compositions, we trained an LSTM model to predict the next piano note given by the preceding sequence of piano notes. After that, we used the classifier we trained without supervision as a data generator to create music. The results of both the text and the music generators are...