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Deep Learning with TensorFlow

Deep Learning with TensorFlow

By : Zaccone, Karim
3 (4)
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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

Chapter 5. Optimizing TensorFlow Autoencoders

In Machine Learning (ML), the so-called curse of dimensionality is a progressive decline in performance with an increase in the input space, often with hundreds or thousands of dimensions, which does not occur in low-dimensional settings such as three-dimensional space. This occurs because the number of samples needed to obtain a sufficient sampling of the input space increases exponentially with the number of dimensions. To overcome this problem, some optimizing networks have been developed.

The first one is autoencoder networks. These are designed and trained to transform an input pattern in itself so that in the presence of a degraded or incomplete version of an input pattern, it is possible to obtain the original pattern. An autoencoder is a Neural Network (NN). The network is trained to create output data like those presented in the entrance and the hidden layer stores the compressed data.

The second optimizing networks are Boltzmann...

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