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|0 (IdRef)195490932
|1 http://www.idref.fr/195490932/id
|a Gulli, Antonio.
|4 aut.
|e Auteur
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|a Deep learning with Keras :
|b implement neural networks with Keras on Theano and TensorFlow
|c Antonio Gulli, Sujit Pal.
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|a Données textuelles.
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|a Birmingham :
|b Packt Publishing.
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|c C 2017.
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|2 RDAfrCarrier
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|a Titre provenant de la p. de titre du document numérisé.
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|a Numérisation de l'édition de : Birmingham : Packt Publishing, cop. 2017.
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|a La pagination de l'édition imprimée correspondante est de 310 p.
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|a Notice rédigée d'après la consultation, 2017-05-22.
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|a L'impression du document génère 310 p.
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|a Cover; Copyright; Credits; About the Authors; About the Reviewer; www.PacktPub.com; Customer Feedback; Table of Contents; Preface; Chapter 1: 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 layersFurther 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 2: Keras Installation and API -- Installing KerasStep 1 -- install some useful dependencies; Step 2 -- install Theano; Step 3 -- install TensorFlow; Step 4 -- install Keras; Step 5 -- testing Theano, TensorFlow, and 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; 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 layersRegularization; 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 3: Deep Learning with ConvNets; Deep convolutional neural network -- DCNN; Local receptive fields; Shared weights and bias; Pooling layers; Max-pooling; Average pooling -- ConvNets summaryAn example of DCNN -- LeNet; LeNet code in Keras; Understanding the power of deep learning; Recognizing CIFAR-10 images with deep learning; Improving the CIFAR-10 performance with deeper a network; Improving the CIFAR-10 performance with data augmentation; Predicting with CIFAR-10; Very deep convolutional networks for large-scale image recognition; Recognizing cats with a VGG-16 net; Utilizing Keras built-in VGG-16 net module; Recycling pre-built deep learning models for extracting features; Very deep inception-v3 net used for transfer learning; Summary
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|a L'accès complet à la ressource est réservé aux usagers des établissements qui en ont fait l'acquisition
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|a Navigateur internet ; lecteur de fichiers PDF.
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