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 model for language modeling

In this section, we will explore what transformers are and build one using PyTorch for the task of language modeling. We will also learn how to use some advanced transformer-based models, such as BERT and GPT, via PyTorch’s pretrained model repository. The pretrained model repository contains PyTorch models trained on general tasks such as language modeling (predicting the next word given the sequence of preceding words). These pretrained models can then be fine-tuned for specific tasks such as sentiment analysis (whether a given piece of writing is positive, negative or neutral). Before we start building a transformer model, let’s quickly recap what language modeling is.

Reviewing language modeling

Language modeling is the task of figuring out the probability of the occurrence of a word or a sequence of words that should follow a given sequence of words. For example, if we are given French is a beautiful _____...