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


In this chapter, we looked at the basic architecture of recurrent neural networks and how they work better than traditional neural networks over sequence data. We saw how RNNs can be used to learn an author's writing style and generate text using the learned model. We also saw how this example can be extended to predicting stock prices or other time series, speech from noisy audio, and so on, as well as generate music that was composed by a learned model.

We looked at different ways to compose our RNN units and these topologies can be used to model and solve specific problems such as sentiment analysis, machine translation, image captioning, and classification, and so on.

We then looked at one of the biggest drawbacks of the SimpleRNN architecture, that of vanishing and exploding gradients. We saw how the vanishing gradient problem is handled using the LSTM (and GRU) architectures. We also looked at the LSTM and GRU architectures in some detail. We also saw two examples of predicting...