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Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
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Deep Learning with TensorFlow

Deep Learning with TensorFlow

3 (4)
By: Zaccone, 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 (13 chapters)
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12
Index

Factorization machines for recommendation systems


In this section, we will see two examples of developing a more robust recommendation system using FMs. We will start with a brief explanation of FM and their application to the cold-start recommendation problem.

Then we will see a short example of using an FM to developing a real-life recommendation system. After that, we will see an example using an improved version of the FM algorithm called a Neural Factorization Machine (NFM).

Factorization machines

FM-based techniques are at the cutting edge of personalization. They have proven to be extremely powerful with enough expressive capacity to generalize existing models, such as matrix/tensor factorization and polynomial kernel regression. In other words, this type of algorithm is a supervised learning approach that enhances the performance of linear models by incorporating second-order feature interactions that are absent in matrix factorization algorithms.

Existing recommendation algorithms require...

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