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

Regression networks


The two major techniques of supervised learning are classification and regression. In both cases, the model is trained with data to predict known labels. In case of classification, these labels are discrete values such as genres of text or image categories. In case of regression, these labels are continuous values, such as stock prices or human intelligence quotients (IQ).

Most of the examples we have seen show deep learning models being used to perform classification. In this section, we will look at how to perform regression using such a model.

Recall that classification models have a dense layer with a nonlinear activation at the end, the output dimension of which corresponds to the number of classes the model can predict. Thus, an ImageNet image classification model has a dense (1,000) layer at the end, corresponding to 1,000 ImageNet classes it can predict. Similarly, a sentiment analysis model has a dense layer at the end, corresponding to positive or negative sentiment...