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

Recommendation Systems with PyTorch

Recommendation systems are everywhere, like Netflix and YouTube, which recommend what to watch; Spotify, which recommends what to listen to; LinkedIn, which suggests jobs; and Amazon, which recommends which products to buy.

Figure 18.1: Examples of different recommendation systems. Starting from top to bottom, followed by left to right – Netflix, Spotify, LinkedIn, YouTube, and Amazon

A recommendation system is an algorithm that provides personalized suggestions to users. The main goal is to predict what product(s) a user may be interested in, based on their preferences, behaviors, similarity with existing users, and interactions with the system. Most of today’s recommendation systems are powered by an underlying deep learning model. Such models predict if a user will like a product (a movie, a book, a podcast, a person on the web, etc.) based on the existing consumption patterns of this and the other users in the system...