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

Chapter 12: 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.

At the end of this chapter, non-experts will be able to design machine learning models with little domain experience, and experts will 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