Book Image

Deep Learning with TensorFlow - Second Edition

By : Giancarlo Zaccone, Md. Rezaul Karim
Book Image

Deep Learning with TensorFlow - Second Edition

By: Giancarlo Zaccone, Md. Rezaul 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 (15 chapters)
Deep Learning with TensorFlow - Second Edition
Contributors
Preface
Other Books You May Enjoy
Index

Summary


In this chapter, we implemented some optimizing networks called autoencoders. An autoencoder is basically a data compression network model. It is used to encode a given input into a representation of smaller dimensions, and then a decoder can be used to reconstruct the input back from the encoded version. All the autoencoders we implemented contain an encoding and a decoding part.

We also saw how to improve the autoencoders' performance by introducing noise during the network training and building a denoising autoencoder. Finally, we applied the concepts of CNNs introduced in Chapter 4, TensorFlow on a Convolutional Neural Network with the implementation of convolutional autoencoders.

Even when the number of hidden units is large, we can still discover the interesting and hidden structure of the dataset using autoencoders by imposing other constraints on the network. In other words, if we impose a sparsity constraint on the hidden units, then the autoencoder will still discover interesting...