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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

Improved factorization machines


Many predictive tasks for web applications need to model categorical variables, such as user IDs, and demographic information, such as genders and occupations. To apply standard ML techniques, these categorical predictors need to be converted to a set of binary features via one-hot encoding (or any other technique). This makes the resultant feature vector highly sparse. To learn effectively from such sparse data, it is important to consider the interactions between features.

In the previous section, we saw that FM could be applied to model second-order feature interactions effectively. However, FM models feature interactions in a linear way, which is insufficient if you want to capture the non-linear and inherently complex structure of real-world data.

Xiangnan He and Jun Xiao et al. have proposed several research initiatives, such as Neural Factorization Machine (NFM) and Attentional Factorization Machine (AFM), in an attempt to overcome this limitation.

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