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

Exploring the evolution of recurrent networks

Recurrent networks have been around since the 80s. In this section, we will explore the evolution of the recurrent network architecture since its inception. We will discuss and reason about the developments that were made to the architecture by going through the key milestones in the evolution of RNNs. Before jumping right into the timelines, we’ll quickly review the different types of RNNs and how they relate to a general feed-forward neural network.

Types of recurrent neural networks

While most supervised machine learning models model one-to-one relationships, RNNs can model the following types of input-output relationships:

  • Many-to-many (instantaneous)

Example: Named entity recognition – Given a sentence/text, tag the words with named entity categories such as names, organizations, locations, and so on.

  • Many-to-many (encoder-decoder)

Example: Machine translation (say, from...