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

PyTorch and AutoML

Automated machine learning (AutoML) provides methods to find the optimal neural architecture and the best hyperparameter settings for a given neural network. We have already covered neural architecture search in detail while discussing the RandWireNN model in Chapter 5, Hybrid Advanced Models.

In this chapter, we will look more broadly at the AutoML tool for PyTorch—Auto-PyTorch—which performs both neural architecture search and hyperparameter search. We will also look at another AutoML tool called Optuna that performs hyperparameter search for a PyTorch model.

By the end of this chapter, non-experts will be able to design machine learning models with little domain experience, and experts will be able to drastically speed up their model selection process.

This chapter is broken down into the following topics:

  • Finding the best neural architectures with AutoML
  • Using Optuna for hyperparameter search

All code...