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

The road ahead

In January 2016, DeepMind announced the release of AlphaGo (for more information refer to: Mastering the Game of Go with Deep Neural Networks and Tree Search, by D. Silver, Nature 529.7587, pp. 484-489, 2016), a neural network to play the game of Go. Go is regarded as a very challenging game for AIs to play, mainly because at any point in the game, there are an average of approximately 10170 possible (for more information refer to: moves (compared with approximately 1050 for chess). Hence determining the best move using brute force methods is computationally infeasible. At the time of publication, AlphaGo had already won 5-0 in a 5-game competition against the current European Go champion, Fan Hui. This was the first time that any computer program had defeated a human player at Go. Subsequently, in March 2016, AlphaGo won 4-1 against Lee Sedol, the world's second professional Go player.

There were several notable new ideas...