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

Understanding and processing the MovieLens dataset

In this section, we dive into the code for creating our recommendation system. As with most ML projects, it all starts with data. We use the MovieLens dataset to create a movie recommendation system.

The MovieLens dataset is a widely used benchmark dataset in the field of recommender systems. It consists of user ratings and movie metadata, providing a rich source for training and evaluating recommendation algorithms. The dataset includes various versions, with MovieLens 100K, 1M, 10M, and 20M being some of the commonly used subsets, differing in the number of ratings and movies. In this chapter, we use the MovieLens 100K dataset, which contains over 100K movie ratings.

As shown in Figure 18.4, we first begin by downloading the dataset. We then load the dataset files as DataFrames, analyze the different DataFrames, and clean the dataset if needed.

Figure 18.4: Steps in exploring, analyzing, and processing the MovieLens...