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 (10 chapters)

Additional Deep Learning Models

So far, most of the discussion has been focused around different models that do classification. These models are trained using object features and their labels to predict labels for hitherto unseen objects. The models also had a fairly simple architecture, all the ones we have seen so far have a linear pipeline modeled by the Keras sequential API.

In this chapter, we will focus on more complex architectures where the pipelines are not necessarily linear. Keras provides the functional API to deal with these sorts of architectures. We will learn how to define our networks using the functional API in this chapter. Note that the functional API can be used to build linear architectures as well.

The simplest extension of classification networks are regression networks. The two broad subcategories under supervised machine learning are classification and regression. Instead of predicting...