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

Summary

RL is one of the fundamental branches of machine learning and is currently one of the hottest, if not the hottest, areas of research and development. RL-based AI breakthroughs such as AlphaGo from Google’s DeepMind have further increased enthusiasm and interest in the field. This chapter provided an overview of RL and DRL and walked us through a hands-on exercise of building a DQN model using PyTorch.

RL is a vast field, and one chapter is not enough to cover everything. I encourage you to use the high-level discussions from this chapter to explore the details around those discussions. From the next chapter onward, we will focus on the practical aspects of working with PyTorch, such as model deployment, parallelized training, automated machine learning, and so on. In the next chapter, we discuss how to effectively train models in PyTorch using distributed training on CPUs, and GPUs, and using mixed precision training on GPUs.