Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow

Gespeichert in:

Autor*in:	Gullì, Antonio - 1971- [verfasserIn] Pal, Sujit [verfasserIn]

Format:	Buch
Sprache:	Englisch

Erschienen:	Birmingham Mumbai: Packt Publishing ; April 2017

Schlagwörter:	Künstliche Intelligenz / Maschinelles Lernen

Systematik:	ST 250 ST 300 ST 301

Umfang:	iv, 303 Seiten ; Illustrationen, Diagramme

Weitere Ausgabe:	Erscheint auch als Online-Ausgabe Gullì, Antonio, 1971 -: Deep learning with Keras - Birmingham : Packt Publishing, 2017

Links:	Inhaltsverzeichnis
ISBN:	978-1-78712-842-2 1-78712-842-3

Katalog-ID:	163003133X

Internformat


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Indexfelder

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fulltext	Deep Learning with Keras Implementing deep learning models and neural networks with the power of Python Antonio Gulli Sujit Pal Packt BIRMINGHAM - MUMBAI Table of Contents Preface Chapter … 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 Activation functions A real example — recognizing handwritten digits One-hot encoding — OHE Defining a simple neural net in Keras Running a simple Keras net and establishing a baseline Improving the simple net in Keras with hidden layers Further improving the simple net in Keras with dropout Testing different optimizers in Keras Increasing the number of epochs Controlling the optimizer learning rate Increasing the number of internal hidden neurons Increasing the size of batch computation Summarizing the experiments run for recognizing handwritten charts Adopting regularization for avoiding overfitting Hyperparameters tuning Predicting output A practical overview of backpropagation Towards a deep learning approach Summary Chapter … Keras Installation and API Installing Keras Step … install some useful dependencies Step … install Theano Step … install TensorFlow Step … install Keras Step … testing Theano, TensorFlow, and Keras i_ … Configuring Keras Installing Keras on Docker Installing Keras on Google Cloud ML Installing Keras on Amazon AWS Installing Keras on Microsoft Azure Keras API Getting started with Keras architecture What is a tensor? Composing models in Keras Sequential composition Functional composition … An overview of predefined neural network layers … Regular dense Recurrent neural networks — simple, LSTM, and GRU Convolutional and pooling layers Regularization Batch normalization … An overview of predefined activation functions An overview of losses functions An overview of metrics An overview of optimizers Some useful operations Saving and loading the weights and the architecture of a model Callbacks for customizing the training process Checkpointing Using TensorBoard and Keras Using Quiver and Keras Summary Chapter … Deep Learning with ConvNets Deep convolutional neural network — DCNN Local receptive fields Shared weights and bias Pooling layers Max-pooling Average pooling ConvNets summary An example of DCNN — LeNet LeNet code in Keras Understanding the power of deep learning Recognizing CIFAR- … images with deep learning Improving the CIFAR- … performance with deeper a network … Improving the CIFAR- … performance with data augmentation Predicting with CIFAR- … Very deep convolutional networks for large-scale image recognition Recognizing cats with a VGG- … net Utilizing Keras built-in VGG- … net module Recycling pre-built deep learning models for extracting features Very deep inception-v3 net used for transfer learning Summary Chapter … Generative Adversarial Networks and WaveNet WhatisaGAN? Some GAN applications 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 … Word Embeddings Distributed representations word2vec The skip-gram word2vec model The CBOW word2vec model Extracting word2vec embeddings from the model Using third-party implementations of word2vec Exploring GloVe Using pre-trained embeddings Learn embeddings from scratch Fine-tuning learned embeddings from word2vec Fine-tune learned embeddings from GloVe Look up embeddings Summary Chapter … Recurrent Neural Network — RNN SimpleRNN cells SimpleRNN with Keras — generating text RNN topologies Vanishing and exploding gradients Long short term memory — LSTM LSTM with Keras — sentiment analysis Gated recurrent unit — GRU … GRU with Keras — POS tagging Bidirectional RNNs Stateful RNNs Stateful LSTM with Keras — predicting electricity consumption Other RNN variants Summary Chapter … Additional Deep Learning Models Keras functional API Regression networks Keras regression example — predicting benzene levels in the air Unsupervised learning — autoencoders Keras autoencoder example — sentence vectors Composing deep networks Keras example — memory network for question answering Customizing Keras Keras example — using the lambda layer Keras example — building a custom normalization layer Generative models Keras example — deep dreaming Keras example — style transfer Summary Chapter … Al Game Playing Reinforcement learning Maximizing future rewards Q-learning The deep Q-network as a Q-function Balancing exploration with exploitation Experience replay, or the value of experience Example - Keras deep Q-network for catch The road ahead Summary Appendix: Conclusion Keras … what is new Installing Keras … API changes Index …
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