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

Composing deep networks

We have looked extensively at these three basic deep learning networks—the fully connected network (FCN), the CNN and the RNN models. While each of these have specific use cases for which they are most suited, you can also compose larger and more useful models by combining these models as Lego-like building blocks and using the Keras functional API to glue them together in new and interesting ways.

Such models tend to be somewhat specialized to the task for which they were built, so it is impossible to generalize about them. Usually, however, they involve learning from multiple inputs or generating multiple outputs. One example could be a question answering network, where the network learns to predict answers given a story and a question. Another example could be a siamese network that calculates similarity between a pair of images, where the network is trained to predict either a binary (similar/not similar) or categorical (gradations of similarity) label using a...