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

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Throughout this book, we have built several deep learning models that can perform different kinds of tasks for us. For example, a handwritten digit classifier, an image-caption generator, a sentiment classifier, and more. Although we have mastered how to train and evaluate these models using PyTorch, we do not know what precisely is happening inside these models while they make predictions. Model interpretability or explainability is that field of machine learning where we aim to answer the question, why did the model make that prediction? More elaborately, what did the model see in the input data to make that particular prediction?

In this chapter, we will use the handwritten digit classification model from Chapter 1, Overview of Deep Learning Using PyTorch, to understand its inner workings and thereby explain why the model makes a certain prediction for a given input. We will first dissect the model using...