Book Image

Mastering PyTorch - Second Edition

By : Ashish Ranjan Jha
4 (1)
Book Image

Mastering PyTorch - Second Edition

4 (1)
By: Ashish Ranjan Jha

Overview of this book

PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch deep learning book will help you uncover expert techniques to get the most out of your data and build complex neural network models. You’ll build convolutional neural networks for image classification and recurrent neural networks 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, including diffusion models. You'll not only build and train your own deep reinforcement learning models in PyTorch but also learn to optimize model training using multiple CPUs, GPUs, and mixed-precision training. You’ll deploy PyTorch models to production, including mobile devices. Finally, you’ll discover the PyTorch ecosystem and its rich set of libraries. These libraries will add another set of tools to your deep learning toolbelt, teaching you how to use fastai to prototype models and PyTorch Lightning to train models. You’ll discover libraries for AutoML and explainable AI (XAI), create recommendation systems, and build language and vision transformers with Hugging Face. By the end of this book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models.
Table of Contents (21 chapters)
20
Index

Music and Text Generation with PyTorch

PyTorch is a fantastic tool for both researching deep learning models and developing deep learning-based applications. In the previous chapters, we looked at model architectures across various domains and model types. We used PyTorch to build these architectures from scratch and used pre-trained models from the PyTorch model zoo. We will switch gears from this chapter onward and dive deep into generative models.

In the previous chapters, most of our examples and exercises revolved around developing models for classification, which is a supervised learning task. However, deep learning models have also proven extremely effective when it comes to unsupervised learning tasks. Deep generative models are one such example. These models are trained using lots of unlabeled data. Once trained, the model can generate similar meaningful data. It does so by learning the underlying structure and patterns in the input data.

In this chapter, we will...