Book Image

Deep Learning with Keras

By : Antonio Gulli, Sujit Pal
Book Image

Deep Learning with Keras

By: Antonio Gulli, Sujit Pal

Overview of this book

This book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and more sophisticated deep convolutional networks. You will also explore image processing with recognition of handwritten digit images, classification of images into different categories, and advanced objects recognition with related image annotations. An example of identification of salient points for face detection is also provided. Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series. Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks (GANs). You will also explore non-traditional uses of neural networks as Style Transfer. Finally, you will look at reinforcement learning and its application to AI game playing, another popular direction of research and application of neural networks.
Table of Contents (16 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Other RNN variants


We will round up this chapter by looking at some more variants of the RNN cell. RNN is an area of active research and many researchers have suggested variants for specific purposes.

One popular LSTM variant is adding peephole connections, which means that the gate layers are allowed to peek at the cell state. This was introduced by Gers and Schmidhuber (for more information refer to the article: Learning Precise Timing with LSTM Recurrent Networks, by F. A. Gers, N. N. Schraudolph, and J. Schmidhuber, Journal of Machine Learning Research, pp. 115-43) in 2002.

Another LSTM variant, that ultimately led to the GRU, is to use coupled forget and output gates. Decisions about what information to forget and what to acquire are made together, and the new information replaces the forgotten information.

Keras provides only the three basic variants, namely the SimpleRNN, LSTM, and GRU layers. However, that isn't necessarily a problem. Gref conducted an experimental survey (for more...