Book Image

Deep Learning with TensorFlow

By : Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy
Book Image

Deep Learning with TensorFlow

By: Giancarlo Zaccone, Md. Rezaul Karim, Ahmed Menshawy

Overview of this book

Deep learning is the step that comes after machine learning, and has more advanced implementations. Machine learning is not just for academics anymore, but is becoming a mainstream practice through wide adoption, and deep learning has taken the front seat. As a data scientist, if you want to explore data abstraction layers, this book will be your guide. This book shows how this can be exploited in the real world with complex raw data using TensorFlow 1.x. Throughout the book, you’ll learn how to implement deep learning algorithms for machine learning systems and integrate them into your product offerings, including search, image recognition, and language processing. Additionally, you’ll learn how to analyze and improve the performance of deep learning models. This can be done by comparing algorithms against benchmarks, along with machine intelligence, to learn from the information and determine ideal behaviors within a specific context. After finishing the book, you will be familiar with machine learning techniques, in particular the use of TensorFlow for deep learning, and will be ready to apply your knowledge to research or commercial projects.
Table of Contents (11 chapters)

Improving autoencoder robustness

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

Intuitively, a denoising autoencoder does two things: first, it tries to encode the input, preserving the concerning information, and then it seeks to nullify the effect of the corruption process applied to the same input.

In the following section, we'll show an implementation of a denoising autoencoder.