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Deep Learning with Keras 1st edition by Antonio Gulli, Sujit Pal ISBN 9781787129030 1787129039

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Product details:

ISBN 10: 1787129039
ISBN 13: 9781787129030
Author:  Antonio Gulli, Sujit Pal 

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:

  • Mission
  • How deep learning is different from machine learning and artificial intelligence
  • What this book covers
  • What you need for this book
  • Who this book is for

Neural Networks Foundations

  • Perceptron
  • The first example of Keras code
  • Multilayer perceptron — the first example of a network
  • Problems in training the perceptron and a solution
  • Activation function — sigmoid
  • Activation function — ReLU

Keras Installation and API

  • Installing Keras
  • Step 1 — install some useful dependencies
  • Step 2 — install Theano
  • Step 3 — install TensorFlow
  • Step 4 — install Keras
  • Step 5 — testing Theano, TensorFlow, and Keras

ng with ConvNets

  • Deep convolutional neural network — DCNN
  • Local receptive fields
  • Shared weights and bias

Generative Adversarial Networks and WaveNet

  • What is a GAN?
  • Some GAN applications

Word Embeddings

  • Distributed representations
  • word2vec
  • The skip-gram word2vec model
  • The CBOW word2vec model

Recurrent Neural Network — RNN

  • SimpleRNN cells
  • SimpleRNN with Keras — generating text
  • RNN topologies
  • Vanishing and exploding gradients

Additional Deep Learning Models

  • Keras functional API
  • Regression networks
  • Keras regression example — predicting benzene levels in the air
  • Unsupervised learning — autoencoders

AI Game Playing

  • Reinforcement learning
  • Maximizing future rewards
  • Q-learning
  • The deep Q-network as a Q-function

Conclusion

  • Keras 2.0 — what is new
  • Installing Keras 2.0
  • API changes

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