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

Summary


In this chapter, we have discussed how to develop scalable recommendation systems with TensorFlow. We have seen some of the theoretical backgrounds of recommendation systems and using a collaborative filtering approach in developing recommendation systems. Later in the chapter, we saw how to use SVD, and K-means, to develop a movie recommendation system.

Finally, we saw how to use FMs and a variation called NFM to develop more accurate recommendation systems that can handle large-scale sparse matrixes. We have seen that the best way to handle the cold-start problem is to use a collaborative filtering approach with FMs.

The next chapter is about designing an ML system driven by criticisms and rewards. We will see how to apply RL algorithms to make a predictive model for real-life datasets.