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

Generative models


Generative models are models that learn to create data similar to data it is trained on. We saw one example of a generative model that learns to write prose similar to Alice in Wonderland in Chapter 6, Recurrent Neural Network — RNN. In that example, we trained a model to predict the 11th character of text given the first 10 characters. Yet another type of generative model is generative adversarial models (GAN) that have recently emerged as a very powerful class of models—you saw examples of GANs in Chapter 4, Generative Adversarial Networks and WaveNet. The intuition for generative models is that it learns a good internal representation of its training data, and is therefore able to generate similar data during the prediction phase.

Another perspective on generative models is the probabilistic one. A typical classification or regression network, also called a discriminative model, learns a function that maps the input data X to some label or output y, that is, these models...