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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

Improving autoencoder robustness

A successful strategy we can use to improve the model's robustness is to introduce a noise in the encoding phase. We call a denoising autoencoder a stochastic version of an autoencoder; in a denoising autoencoder, the input is stochastically corrupted, but the uncorrupted version of the same input is used as the target for the decoding phase.

Intuitively, a denoising autoencoder does two things: first, it tries to encode the input, preserving the relevant information; and then, it seeks to nullify the effect of the corruption process applied to the same input. In the next section, we'll show an implementation of a denoising autoencoder.

Implementing a denoising autoencoder

The network architecture is very simple. A 784-pixel input image is stochastically corrupted and then dimensionally reduced by an encoding network layer. The image size is reduced from 784 to 256 pixels.

In the decoding phase, we prepare the network for output, returning the image...

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