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)

Summary

In this chapter, we have 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 a smaller dimension; 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 have also looked at how to improve the autoencoder's performance, introducing a noise during network training, and building a denoising autoencoder. Finally, we applied the concepts of the CNN networks introduced in Chapter 4, TensorFlow on a Convolutional Neural Network, with the implementation of convolutional autoencoders.

In the next chapter, we'll examine Recurrent Neural Networks (RNNs). We will start by describing the basic principles of these networks, and then we'll implement some interesting example...