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
About the Authors
About the Reviewer
Customer Feedback

Chapter 6. Recurrent Neural Network — RNN

In Chapter 3, Deep Learning with ConvNets, we learned about convolutional neural networks (CNN) and saw how they exploit the spatial geometry of their input. For example, CNNs apply convolution and pooling operations in one dimension for audio and text data along the time dimension, in two dimensions for images along the (height x width) dimensions and in three dimensions, for videos along the (height x width x time) dimensions.

In this chapter, we will learn about recurrent neural networks (RNN), a class of neural networks that exploit the sequential nature of their input. Such inputs could be text, speech, time series, and anything else where the occurrence of an element in the sequence is dependent on the elements that appeared before it. For example, the next word in the sentence the dog... is more likely to be barks than car, therefore, given such a sequence, an RNN is more likely to predict barks than car.

An RNN can be thought of as a graph...