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

SimpleRNN cells


Traditional multilayer perceptron neural networks make the assumption that all inputs are independent of each other. This assumption breaks down in the case of sequence data. You have already seen the example in the previous section where the first two words in the sentence affect the third. The same idea is true of speech—if we are having a conversation in a noisy room, I can make reasonable guesses about a word I may not have understood based on the words I have heard so far. Time series data, such as stock prices or weather, also exhibit a dependence on past data, called the secular trend.

RNN cells incorporate this dependence by having a hidden state, or memory, that holds the essence of what has been seen so far. The value of the hidden state at any point in time is a function of the value of the hidden state at the previous time step and the value of the input at the current time step, that is:

ht and ht-1 are the values of the hidden states at the time steps t and t...