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

Using deep learning for recommendation systems

In this section, we will learn how to use deep learning to create a recommendation system. We will use the example of a movie recommendation system to understand the underlying concepts. First, we will review what movie recommendation system data looks like, and we will then discuss a deep learning-based solution to build a recommendation system.

Understanding a movie recommendation system dataset

The dataset for building a recommendation system looks like the toy example shown in Figure 18.2. On the vertical axis, we have 5 users in our database. And on the horizontal axis, we have 8 movies.

Figure 18.2: Example of a movie database presented as a user-movie matrix, where the entries of the matrix represent the rating given by the user to the movie

For a given user and a given movie, we find a rating ranging from 1 to 5 stars, 1 being the worst and 5 being the best. This rating is used as a target to train a deep...