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

Building a transformer-based text generator with PyTorch

We built a transformer-based language model using PyTorch in the previous chapter. Because a language model models the probability of a certain word following a given sequence of words, we are more than halfway through building our own text generator. In this section, we will learn how to extend this language model as a deep generative model that can generate arbitrary yet meaningful sentences, given an initial textual cue in the form of a sequence of words.

Training the transformer-based language model

In the previous chapter, we trained a language model for 5 epochs. In this section, we will follow those exact same steps but will train the model for longer – 50 epochs. The goal here is to obtain a better-performing language model that can then generate realistic sentences. Please note that model training can take several hours. Hence, train it in the background; for example, overnight. In order to follow the...