Deep Learning with Keras

Get to grips with the basics of Keras to implement fast and efficient deep-learning models

Deep Learning with Keras

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Antonio Gulli, Sujit Pal

Get to grips with the basics of Keras to implement fast and efficient deep-learning models
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Book Details

ISBN 139781787128422
Paperback318 pages

Book Description

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 hand written 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 (GAN). 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

Chapter 1: Neural Networks Foundations
Perceptron
Multilayer perceptron — the first example of a network
A real example — recognizing handwritten digits
A practical overview of backpropagation
Towards a deep learning approach
Summary
Chapter 2: Keras Installation and API
Installing Keras
Configuring Keras
Installing Keras on Docker
Installing Keras on Google Cloud ML
Installing Keras on Amazon AWS
Installing Keras on Microsoft Azure
Keras API
Callbacks for customizing the training process
Summary
Chapter 3: Deep Learning with ConvNets
Deep convolutional neural network — DCNN
An example of DCNN — LeNet
Recognizing CIFAR-10 images with deep learning
Very deep convolutional networks for large-scale image recognition
Summary
Chapter 4: Generative Adversarial Networks and WaveNet
What is a GAN?
Deep convolutional generative adversarial networks
Keras adversarial GANs for forging MNIST
Keras adversarial GANs for forging CIFAR
WaveNet — a generative model for learning how to produce audio
Summary
Chapter 5: Word Embeddings
Distributed representations
word2vec
Exploring GloVe
Using pre-trained embeddings
Summary
Chapter 6: Recurrent Neural Network — RNN
SimpleRNN cells
RNN topologies
Vanishing and exploding gradients
Long short term memory — LSTM
Gated recurrent unit — GRU
Bidirectional RNNs
Stateful RNNs
Other RNN variants
Summary
Chapter 7: Additional Deep Learning Models
Keras functional API
Regression networks
Unsupervised learning — autoencoders
Composing deep networks
Customizing Keras
Generative models
Summary
Chapter 8: AI Game Playing
Reinforcement learning
Example - Keras deep Q-network for catch
The road ahead
Summary
Chapter 9: Conclusion
Keras 2.0 — what is new

What You Will Learn

  • Optimize step-by-step functions on a large neural network using the Backpropagation Algorithm
  • Fine-tune a neural network to improve the quality of results
  • Use deep learning for image and audio processing
  • Use Recursive Neural Tensor Networks (RNTNs) to outperform standard word embedding in special cases
  • Identify problems for which Recurrent Neural Network (RNN) solutions are suitable
  • Explore the process required to implement Autoencoders
  • Evolve a deep neural network using reinforcement learning

Authors

Table of Contents

Chapter 1: Neural Networks Foundations
Perceptron
Multilayer perceptron — the first example of a network
A real example — recognizing handwritten digits
A practical overview of backpropagation
Towards a deep learning approach
Summary
Chapter 2: Keras Installation and API
Installing Keras
Configuring Keras
Installing Keras on Docker
Installing Keras on Google Cloud ML
Installing Keras on Amazon AWS
Installing Keras on Microsoft Azure
Keras API
Callbacks for customizing the training process
Summary
Chapter 3: Deep Learning with ConvNets
Deep convolutional neural network — DCNN
An example of DCNN — LeNet
Recognizing CIFAR-10 images with deep learning
Very deep convolutional networks for large-scale image recognition
Summary
Chapter 4: Generative Adversarial Networks and WaveNet
What is a GAN?
Deep convolutional generative adversarial networks
Keras adversarial GANs for forging MNIST
Keras adversarial GANs for forging CIFAR
WaveNet — a generative model for learning how to produce audio
Summary
Chapter 5: Word Embeddings
Distributed representations
word2vec
Exploring GloVe
Using pre-trained embeddings
Summary
Chapter 6: Recurrent Neural Network — RNN
SimpleRNN cells
RNN topologies
Vanishing and exploding gradients
Long short term memory — LSTM
Gated recurrent unit — GRU
Bidirectional RNNs
Stateful RNNs
Other RNN variants
Summary
Chapter 7: Additional Deep Learning Models
Keras functional API
Regression networks
Unsupervised learning — autoencoders
Composing deep networks
Customizing Keras
Generative models
Summary
Chapter 8: AI Game Playing
Reinforcement learning
Example - Keras deep Q-network for catch
The road ahead
Summary
Chapter 9: Conclusion
Keras 2.0 — what is new

Book Details

ISBN 139781787128422
Paperback318 pages
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