Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul Karim

Overview of this book

Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects.
Table of Contents (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Movie recommendation using collaborative filtering


In this section, we will see how to utilize collaborative filtering to develop a recommendation engine. However, before that let's discuss the utility matrix of preferences.

The utility matrix

In a collaborative filtering-based recommendation system, there are dimensions of entities: users and items (items refer to products, such as movies, games, and songs). As a user, you might have preferences for certain items. Therefore, these preferences must be extracted out of the data about items, users, or ratings. This data is often represented as a utility matrix, such as a user-item pair. This type of value can represent what is known about the degree of preference that the user has for a particular item.

The entry in the matrix can come from an ordered set. For example, integers 1-5 can be used to represent the number of stars that the user gave when rating items. We have already mentioned that users might not rate items very often, so most entries...