TensorFlow 1.x deep learning cookbook :: over 90 unique recipes to solve artificial-intelligence driven problems with Python /
Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilaye...
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| Format: | Elektronisch E-Book |
| Sprache: | English |
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Packt Publishing,
2017.
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| Zusammenfassung: | Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learnin ... |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| ISBN: | 9781788291866 1788291867 1788293592 9781788293594 |
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| 520 | |a Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learnin ... | ||
| 505 | 0 | |a Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Dedication -- Table of Contents -- Preface -- Chapter 1: TensorFlow -- An Introduction -- Introduction -- Installing TensorFlow -- Getting ready -- How to do it... -- How it works... -- There's more... -- Hello world in TensorFlow -- How to do it... -- How it works... -- Understanding the TensorFlow program structure -- How to do it... -- How it works... -- There's more... -- Working with constants, variables, and placeholders -- How to do it... -- How it works... -- There's more... -- Performing matrix manipulations using TensorFlow -- How to do it... -- How it works... -- There's more... -- Using a data flow graph -- How to do it... -- Migrating from 0.x to 1.x -- How to do it... -- There's more... -- Using XLA to enhance computational performance -- Getting ready -- How to do it... -- Invoking CPU/GPU devices -- How to do it... -- How it works... -- TensorFlow for Deep Learning -- How to do it... -- There's more -- Different Python packages required for DNN-based problems -- How to do it... -- See also -- Chapter 2: Regression -- Introduction -- Choosing loss functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizers in TensorFlow -- Getting ready -- How to do it... -- There's more... -- See also -- Reading from CSV files and preprocessing data -- Getting ready -- How to do it... -- There's more... -- House price estimation-simple linear regression -- Getting ready -- How to do it... -- How it works... -- There's more... -- House price estimation-multiple linear regression -- How to do it... -- How it works... -- There's more... -- Logistic regression on the MNIST dataset -- How to do it... -- How it works... -- See also -- Chapter 3: Neural Networks -- Perceptron -- Introduction. | |
| 505 | 8 | |a Activation functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Single layer perceptron -- Getting ready -- How to do it... -- There's more... -- Calculating gradients of backpropagation algorithm -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- MNIST classifier using MLP -- Getting ready -- How to do it... -- How it works... -- Function approximation using MLP-predicting Boston house prices -- Getting ready -- How to do it... -- How it works... -- There's more... -- Tuning hyperparameters -- How to do it... -- There's more... -- See also -- Higher-level APIs-Keras -- How to do it... -- There's more... -- See also -- Chapter 4: Convolutional Neural Networks -- Introduction -- Local receptive fields -- Shared weights and bias -- A mathematical example -- ConvNets in TensorFlow -- Pooling layers -- Max pooling -- Average pooling -- ConvNets summary -- Creating a ConvNet to classify handwritten MNIST numbers -- Getting ready -- How to do it... -- How it works... -- Creating a ConvNet to classify CIFAR-10 -- Getting ready -- How to do it... -- How it works... -- There's more... -- Transferring style with VGG19 for image repainting -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using a pretrained VGG16 net for transfer learning -- Getting ready -- How to do it... -- How it works... -- There's more... -- Creating a DeepDream network -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Chapter 5: Advanced Convolutional Neural Networks -- Introduction -- Creating a ConvNet for Sentiment Analysis -- Getting ready -- How to do it... -- How it works... -- There is more... -- Inspecting what filters a VGG pre-built network has learned -- Getting ready -- How to do it... -- How it works... -- There is more. | |
| 505 | 8 | |a Classifying images with VGGNet, ResNet, Inception, and Xception -- VGG16 and VGG19 -- ResNet -- Inception -- Xception -- Getting ready -- How to do it... -- How it works... -- There is more... -- Recycling pre-built Deep Learning models for extracting features -- Getting ready -- How to do it... -- How it works... -- Very deep InceptionV3 Net used for Transfer Learning -- Getting ready -- How to do it... -- How it works... -- There is more... -- Generating music with dilated ConvNets, WaveNet, and NSynth -- Getting ready -- How to do it... -- How it works... -- There is more... -- Answering questions about images (Visual Q& -- A) -- How to do it... -- How it works... -- There is more... -- Classifying videos with pre-trained nets in six different ways -- How to do it... -- How it works... -- There is more... -- Chapter 6: Recurrent Neural Networks -- Introduction -- Vanishing and exploding gradients -- Long Short Term Memory (LSTM) -- Gated Recurrent Units (GRUs) and Peephole LSTM -- Operating on sequences of vectors -- Neural machine translation -- training a seq2seq RNN -- Getting ready -- How to do it... -- How it works... -- Neural machine translation -- inference on a seq2seq RNN -- How to do it... -- How it works... -- All you need is attention -- another example of a seq2seq RNN -- How to do it... -- How it works... -- There's more... -- Learning to write as Shakespeare with RNNs -- How to do it... -- How it works... -- First iteration -- After a few iterations -- There's more... -- Learning to predict future Bitcoin value with RNNs -- How to do it... -- How it works... -- There's more... -- Many-to-one and many-to-many RNN examples -- How to do it... -- How it works... -- Chapter 7: Unsupervised Learning -- Introduction -- Principal component analysis -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also. | |
| 505 | 8 | |a K-means clustering -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Self-organizing maps -- Getting ready -- How to do it... -- How it works... -- See also -- Restricted Boltzmann Machine -- Getting ready -- How to do it... -- How it works... -- See also -- Recommender system using RBM -- Getting ready -- How to do it... -- There's more... -- DBN for Emotion Detection -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 8: Autoencoders -- Introduction -- See Also -- Vanilla autoencoders -- Getting ready -- How to do it... -- How it works... -- There's more... -- Sparse autoencoder -- Getting Ready... -- How to do it... -- How it works... -- There's More... -- See Also -- Denoising autoencoder -- Getting Ready -- How to do it... -- See Also -- Convolutional autoencoders -- Getting Ready... -- How to do it... -- How it Works... -- There's More... -- See Also -- Stacked autoencoder -- Getting Ready -- How to do it... -- How it works... -- There's More... -- See Also -- Chapter 9: Reinforcement Learning -- Introduction -- Learning OpenAI Gym -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Implementing neural network agent to play Pac-Man -- Getting ready -- How to do it... -- Q learning to balance Cart-Pole -- Getting ready -- How to do it... -- There's more... -- See also -- Game of Atari using Deep Q Networks -- Getting ready -- How to do it... -- There's more... -- See also -- Policy gradients to play the game of Pong -- Getting ready -- How to do it... -- How it works... -- There's more... -- AlphaGo Zero -- See also -- Chapter 10: Mobile Computation -- Introduction -- TensorFlow, mobile, and the cloud -- Installing TensorFlow mobile for macOS and Android -- Getting ready -- How to do it... -- How it works... -- There's more. | |
| 505 | 8 | |a Playing with TensorFlow and Android examples -- Getting ready -- How to do it... -- How it works... -- Installing TensorFlow mobile for macOS and iPhone -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizing a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Profiling a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Transforming a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Chapter 11: Generative Models and CapsNet -- Introduction -- So what is a GAN? -- Some cool GAN applications -- Learning to forge MNIST images with simple GANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge MNIST images with DCGANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge Celebrity Faces and other datasets with DCGAN -- Getting ready -- How to do it... -- How it works... -- There's more... -- Implementing Variational Autoencoders -- Getting ready... -- How to do it... -- How it works... -- There's More... -- See also... -- Learning to beat the previous MNIST state-of-the-art results with Capsule Networks -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 12: Distributed TensorFlow and Cloud Deep Learning -- Introduction -- Working with TensorFlow and GPUs -- Getting ready -- How to do it... -- How it works... -- Playing with Distributed TensorFlow: multiple GPUs and one CPU -- Getting ready -- How to do it... -- How it works... -- Playing with Distributed TensorFlow: multiple servers -- Getting ready -- How to do it... -- How it works... -- There is more... -- Training a Distributed TensorFlow MNIST classifier -- Getting ready -- How to do it... -- How it works... -- Working with TensorFlow Serving and Docker. | |
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| contents | Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Dedication -- Table of Contents -- Preface -- Chapter 1: TensorFlow -- An Introduction -- Introduction -- Installing TensorFlow -- Getting ready -- How to do it... -- How it works... -- There's more... -- Hello world in TensorFlow -- How to do it... -- How it works... -- Understanding the TensorFlow program structure -- How to do it... -- How it works... -- There's more... -- Working with constants, variables, and placeholders -- How to do it... -- How it works... -- There's more... -- Performing matrix manipulations using TensorFlow -- How to do it... -- How it works... -- There's more... -- Using a data flow graph -- How to do it... -- Migrating from 0.x to 1.x -- How to do it... -- There's more... -- Using XLA to enhance computational performance -- Getting ready -- How to do it... -- Invoking CPU/GPU devices -- How to do it... -- How it works... -- TensorFlow for Deep Learning -- How to do it... -- There's more -- Different Python packages required for DNN-based problems -- How to do it... -- See also -- Chapter 2: Regression -- Introduction -- Choosing loss functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizers in TensorFlow -- Getting ready -- How to do it... -- There's more... -- See also -- Reading from CSV files and preprocessing data -- Getting ready -- How to do it... -- There's more... -- House price estimation-simple linear regression -- Getting ready -- How to do it... -- How it works... -- There's more... -- House price estimation-multiple linear regression -- How to do it... -- How it works... -- There's more... -- Logistic regression on the MNIST dataset -- How to do it... -- How it works... -- See also -- Chapter 3: Neural Networks -- Perceptron -- Introduction. Activation functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Single layer perceptron -- Getting ready -- How to do it... -- There's more... -- Calculating gradients of backpropagation algorithm -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- MNIST classifier using MLP -- Getting ready -- How to do it... -- How it works... -- Function approximation using MLP-predicting Boston house prices -- Getting ready -- How to do it... -- How it works... -- There's more... -- Tuning hyperparameters -- How to do it... -- There's more... -- See also -- Higher-level APIs-Keras -- How to do it... -- There's more... -- See also -- Chapter 4: Convolutional Neural Networks -- Introduction -- Local receptive fields -- Shared weights and bias -- A mathematical example -- ConvNets in TensorFlow -- Pooling layers -- Max pooling -- Average pooling -- ConvNets summary -- Creating a ConvNet to classify handwritten MNIST numbers -- Getting ready -- How to do it... -- How it works... -- Creating a ConvNet to classify CIFAR-10 -- Getting ready -- How to do it... -- How it works... -- There's more... -- Transferring style with VGG19 for image repainting -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using a pretrained VGG16 net for transfer learning -- Getting ready -- How to do it... -- How it works... -- There's more... -- Creating a DeepDream network -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Chapter 5: Advanced Convolutional Neural Networks -- Introduction -- Creating a ConvNet for Sentiment Analysis -- Getting ready -- How to do it... -- How it works... -- There is more... -- Inspecting what filters a VGG pre-built network has learned -- Getting ready -- How to do it... -- How it works... -- There is more. Classifying images with VGGNet, ResNet, Inception, and Xception -- VGG16 and VGG19 -- ResNet -- Inception -- Xception -- Getting ready -- How to do it... -- How it works... -- There is more... -- Recycling pre-built Deep Learning models for extracting features -- Getting ready -- How to do it... -- How it works... -- Very deep InceptionV3 Net used for Transfer Learning -- Getting ready -- How to do it... -- How it works... -- There is more... -- Generating music with dilated ConvNets, WaveNet, and NSynth -- Getting ready -- How to do it... -- How it works... -- There is more... -- Answering questions about images (Visual Q& -- A) -- How to do it... -- How it works... -- There is more... -- Classifying videos with pre-trained nets in six different ways -- How to do it... -- How it works... -- There is more... -- Chapter 6: Recurrent Neural Networks -- Introduction -- Vanishing and exploding gradients -- Long Short Term Memory (LSTM) -- Gated Recurrent Units (GRUs) and Peephole LSTM -- Operating on sequences of vectors -- Neural machine translation -- training a seq2seq RNN -- Getting ready -- How to do it... -- How it works... -- Neural machine translation -- inference on a seq2seq RNN -- How to do it... -- How it works... -- All you need is attention -- another example of a seq2seq RNN -- How to do it... -- How it works... -- There's more... -- Learning to write as Shakespeare with RNNs -- How to do it... -- How it works... -- First iteration -- After a few iterations -- There's more... -- Learning to predict future Bitcoin value with RNNs -- How to do it... -- How it works... -- There's more... -- Many-to-one and many-to-many RNN examples -- How to do it... -- How it works... -- Chapter 7: Unsupervised Learning -- Introduction -- Principal component analysis -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also. K-means clustering -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Self-organizing maps -- Getting ready -- How to do it... -- How it works... -- See also -- Restricted Boltzmann Machine -- Getting ready -- How to do it... -- How it works... -- See also -- Recommender system using RBM -- Getting ready -- How to do it... -- There's more... -- DBN for Emotion Detection -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 8: Autoencoders -- Introduction -- See Also -- Vanilla autoencoders -- Getting ready -- How to do it... -- How it works... -- There's more... -- Sparse autoencoder -- Getting Ready... -- How to do it... -- How it works... -- There's More... -- See Also -- Denoising autoencoder -- Getting Ready -- How to do it... -- See Also -- Convolutional autoencoders -- Getting Ready... -- How to do it... -- How it Works... -- There's More... -- See Also -- Stacked autoencoder -- Getting Ready -- How to do it... -- How it works... -- There's More... -- See Also -- Chapter 9: Reinforcement Learning -- Introduction -- Learning OpenAI Gym -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Implementing neural network agent to play Pac-Man -- Getting ready -- How to do it... -- Q learning to balance Cart-Pole -- Getting ready -- How to do it... -- There's more... -- See also -- Game of Atari using Deep Q Networks -- Getting ready -- How to do it... -- There's more... -- See also -- Policy gradients to play the game of Pong -- Getting ready -- How to do it... -- How it works... -- There's more... -- AlphaGo Zero -- See also -- Chapter 10: Mobile Computation -- Introduction -- TensorFlow, mobile, and the cloud -- Installing TensorFlow mobile for macOS and Android -- Getting ready -- How to do it... -- How it works... -- There's more. Playing with TensorFlow and Android examples -- Getting ready -- How to do it... -- How it works... -- Installing TensorFlow mobile for macOS and iPhone -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizing a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Profiling a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Transforming a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Chapter 11: Generative Models and CapsNet -- Introduction -- So what is a GAN? -- Some cool GAN applications -- Learning to forge MNIST images with simple GANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge MNIST images with DCGANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge Celebrity Faces and other datasets with DCGAN -- Getting ready -- How to do it... -- How it works... -- There's more... -- Implementing Variational Autoencoders -- Getting ready... -- How to do it... -- How it works... -- There's More... -- See also... -- Learning to beat the previous MNIST state-of-the-art results with Capsule Networks -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 12: Distributed TensorFlow and Cloud Deep Learning -- Introduction -- Working with TensorFlow and GPUs -- Getting ready -- How to do it... -- How it works... -- Playing with Distributed TensorFlow: multiple GPUs and one CPU -- Getting ready -- How to do it... -- How it works... -- Playing with Distributed TensorFlow: multiple servers -- Getting ready -- How to do it... -- How it works... -- There is more... -- Training a Distributed TensorFlow MNIST classifier -- Getting ready -- How to do it... -- How it works... -- Working with TensorFlow Serving and Docker. |
| ctrlnum | (OCoLC)1020288466 |
| dewey-full | 005.133 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.133 |
| dewey-search | 005.133 |
| dewey-sort | 15.133 |
| dewey-tens | 000 - Computer science, information, general works |
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title from PDF title page (EBSCO, viewed April 18, 2018)</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learnin ...</subfield></datafield><datafield tag="505" ind1="0" ind2=" "><subfield code="a">Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Dedication -- Table of Contents -- Preface -- Chapter 1: TensorFlow -- An Introduction -- Introduction -- Installing TensorFlow -- Getting ready -- How to do it... -- How it works... -- There's more... -- Hello world in TensorFlow -- How to do it... -- How it works... -- Understanding the TensorFlow program structure -- How to do it... -- How it works... -- There's more... -- Working with constants, variables, and placeholders -- How to do it... -- How it works... -- There's more... -- Performing matrix manipulations using TensorFlow -- How to do it... -- How it works... -- There's more... -- Using a data flow graph -- How to do it... -- Migrating from 0.x to 1.x -- How to do it... -- There's more... -- Using XLA to enhance computational performance -- Getting ready -- How to do it... -- Invoking CPU/GPU devices -- How to do it... -- How it works... -- TensorFlow for Deep Learning -- How to do it... -- There's more -- Different Python packages required for DNN-based problems -- How to do it... -- See also -- Chapter 2: Regression -- Introduction -- Choosing loss functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizers in TensorFlow -- Getting ready -- How to do it... -- There's more... -- See also -- Reading from CSV files and preprocessing data -- Getting ready -- How to do it... -- There's more... -- House price estimation-simple linear regression -- Getting ready -- How to do it... -- How it works... -- There's more... -- House price estimation-multiple linear regression -- How to do it... -- How it works... -- There's more... -- Logistic regression on the MNIST dataset -- How to do it... -- How it works... -- See also -- Chapter 3: Neural Networks -- Perceptron -- Introduction.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Activation functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Single layer perceptron -- Getting ready -- How to do it... -- There's more... -- Calculating gradients of backpropagation algorithm -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- MNIST classifier using MLP -- Getting ready -- How to do it... -- How it works... -- Function approximation using MLP-predicting Boston house prices -- Getting ready -- How to do it... -- How it works... -- There's more... -- Tuning hyperparameters -- How to do it... -- There's more... -- See also -- Higher-level APIs-Keras -- How to do it... -- There's more... -- See also -- Chapter 4: Convolutional Neural Networks -- Introduction -- Local receptive fields -- Shared weights and bias -- A mathematical example -- ConvNets in TensorFlow -- Pooling layers -- Max pooling -- Average pooling -- ConvNets summary -- Creating a ConvNet to classify handwritten MNIST numbers -- Getting ready -- How to do it... -- How it works... -- Creating a ConvNet to classify CIFAR-10 -- Getting ready -- How to do it... -- How it works... -- There's more... -- Transferring style with VGG19 for image repainting -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using a pretrained VGG16 net for transfer learning -- Getting ready -- How to do it... -- How it works... -- There's more... -- Creating a DeepDream network -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Chapter 5: Advanced Convolutional Neural Networks -- Introduction -- Creating a ConvNet for Sentiment Analysis -- Getting ready -- How to do it... -- How it works... -- There is more... -- Inspecting what filters a VGG pre-built network has learned -- Getting ready -- How to do it... -- How it works... -- There is more.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Classifying images with VGGNet, ResNet, Inception, and Xception -- VGG16 and VGG19 -- ResNet -- Inception -- Xception -- Getting ready -- How to do it... -- How it works... -- There is more... -- Recycling pre-built Deep Learning models for extracting features -- Getting ready -- How to do it... -- How it works... -- Very deep InceptionV3 Net used for Transfer Learning -- Getting ready -- How to do it... -- How it works... -- There is more... -- Generating music with dilated ConvNets, WaveNet, and NSynth -- Getting ready -- How to do it... -- How it works... -- There is more... -- Answering questions about images (Visual Q&amp -- A) -- How to do it... -- How it works... -- There is more... -- Classifying videos with pre-trained nets in six different ways -- How to do it... -- How it works... -- There is more... -- Chapter 6: Recurrent Neural Networks -- Introduction -- Vanishing and exploding gradients -- Long Short Term Memory (LSTM) -- Gated Recurrent Units (GRUs) and Peephole LSTM -- Operating on sequences of vectors -- Neural machine translation -- training a seq2seq RNN -- Getting ready -- How to do it... -- How it works... -- Neural machine translation -- inference on a seq2seq RNN -- How to do it... -- How it works... -- All you need is attention -- another example of a seq2seq RNN -- How to do it... -- How it works... -- There's more... -- Learning to write as Shakespeare with RNNs -- How to do it... -- How it works... -- First iteration -- After a few iterations -- There's more... -- Learning to predict future Bitcoin value with RNNs -- How to do it... -- How it works... -- There's more... -- Many-to-one and many-to-many RNN examples -- How to do it... -- How it works... -- Chapter 7: Unsupervised Learning -- Introduction -- Principal component analysis -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">K-means clustering -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Self-organizing maps -- Getting ready -- How to do it... -- How it works... -- See also -- Restricted Boltzmann Machine -- Getting ready -- How to do it... -- How it works... -- See also -- Recommender system using RBM -- Getting ready -- How to do it... -- There's more... -- DBN for Emotion Detection -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 8: Autoencoders -- Introduction -- See Also -- Vanilla autoencoders -- Getting ready -- How to do it... -- How it works... -- There's more... -- Sparse autoencoder -- Getting Ready... -- How to do it... -- How it works... -- There's More... -- See Also -- Denoising autoencoder -- Getting Ready -- How to do it... -- See Also -- Convolutional autoencoders -- Getting Ready... -- How to do it... -- How it Works... -- There's More... -- See Also -- Stacked autoencoder -- Getting Ready -- How to do it... -- How it works... -- There's More... -- See Also -- Chapter 9: Reinforcement Learning -- Introduction -- Learning OpenAI Gym -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Implementing neural network agent to play Pac-Man -- Getting ready -- How to do it... -- Q learning to balance Cart-Pole -- Getting ready -- How to do it... -- There's more... -- See also -- Game of Atari using Deep Q Networks -- Getting ready -- How to do it... -- There's more... -- See also -- Policy gradients to play the game of Pong -- Getting ready -- How to do it... -- How it works... -- There's more... -- AlphaGo Zero -- See also -- Chapter 10: Mobile Computation -- Introduction -- TensorFlow, mobile, and the cloud -- Installing TensorFlow mobile for macOS and Android -- Getting ready -- How to do it... -- How it works... -- There's more.</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Playing with TensorFlow and Android examples -- Getting ready -- How to do it... -- How it works... -- Installing TensorFlow mobile for macOS and iPhone -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizing a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Profiling a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Transforming a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Chapter 11: Generative Models and CapsNet -- Introduction -- So what is a GAN? -- Some cool GAN applications -- Learning to forge MNIST images with simple GANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge MNIST images with DCGANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge Celebrity Faces and other 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| id | ZDB-4-EBA-on1020288466 |
| illustrated | Illustrated |
| indexdate | 2024-11-27T13:28:11Z |
| institution | BVB |
| isbn | 9781788291866 1788291867 1788293592 9781788293594 |
| language | English |
| oclc_num | 1020288466 |
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| spelling | Gulli, Antonio, author. TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python / Antonio Gulli, Amita Kapoor. Birmingham, UK : Packt Publishing, 2017. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier data file Online resource; title from PDF title page (EBSCO, viewed April 18, 2018) Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x About This Book Skill up and implement tricky neural networks using Google's TensorFlow 1.x An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more. Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment Who This Book Is For This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful. What You Will Learn Install TensorFlow and use it for CPU and GPU operations Implement DNNs and apply them to solve different AI-driven problems. Leverage different data sets such as MNIST, CIFAR-10, and Youtube8m with TensorFlow and learn how to access and use them in your code. Use TensorBoard to understand neural network architectures, optimize the learning process, and peek inside the neural network black box. Use different regression techniques for prediction and classification problems Build single and multilayer perceptrons in TensorFlow Implement CNN and RNN in TensorFlow, and use it to solve real-world use cases. Learn how restricted Boltzmann Machines can be used to recommend movies. Understand the implementation of Autoencoders and deep belief networks, and use them for emotion detection. Master the different reinforcement learning methods to implement game playing agents. GANs and their implementation using TensorFlow. In Detail Deep neural networks (DNNs) have achieved a lot of success in the field of computer vision, speech recognition, and natural language processing. The entire world is filled with excitement about how deep networks are revolutionizing artificial intelligence. This exciting recipe-based guide will take you from the realm of DNN theory to implementing them practically to solve the real-life problems in artificial intelligence domain. In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learnin ... Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Dedication -- Table of Contents -- Preface -- Chapter 1: TensorFlow -- An Introduction -- Introduction -- Installing TensorFlow -- Getting ready -- How to do it... -- How it works... -- There's more... -- Hello world in TensorFlow -- How to do it... -- How it works... -- Understanding the TensorFlow program structure -- How to do it... -- How it works... -- There's more... -- Working with constants, variables, and placeholders -- How to do it... -- How it works... -- There's more... -- Performing matrix manipulations using TensorFlow -- How to do it... -- How it works... -- There's more... -- Using a data flow graph -- How to do it... -- Migrating from 0.x to 1.x -- How to do it... -- There's more... -- Using XLA to enhance computational performance -- Getting ready -- How to do it... -- Invoking CPU/GPU devices -- How to do it... -- How it works... -- TensorFlow for Deep Learning -- How to do it... -- There's more -- Different Python packages required for DNN-based problems -- How to do it... -- See also -- Chapter 2: Regression -- Introduction -- Choosing loss functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizers in TensorFlow -- Getting ready -- How to do it... -- There's more... -- See also -- Reading from CSV files and preprocessing data -- Getting ready -- How to do it... -- There's more... -- House price estimation-simple linear regression -- Getting ready -- How to do it... -- How it works... -- There's more... -- House price estimation-multiple linear regression -- How to do it... -- How it works... -- There's more... -- Logistic regression on the MNIST dataset -- How to do it... -- How it works... -- See also -- Chapter 3: Neural Networks -- Perceptron -- Introduction. Activation functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Single layer perceptron -- Getting ready -- How to do it... -- There's more... -- Calculating gradients of backpropagation algorithm -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- MNIST classifier using MLP -- Getting ready -- How to do it... -- How it works... -- Function approximation using MLP-predicting Boston house prices -- Getting ready -- How to do it... -- How it works... -- There's more... -- Tuning hyperparameters -- How to do it... -- There's more... -- See also -- Higher-level APIs-Keras -- How to do it... -- There's more... -- See also -- Chapter 4: Convolutional Neural Networks -- Introduction -- Local receptive fields -- Shared weights and bias -- A mathematical example -- ConvNets in TensorFlow -- Pooling layers -- Max pooling -- Average pooling -- ConvNets summary -- Creating a ConvNet to classify handwritten MNIST numbers -- Getting ready -- How to do it... -- How it works... -- Creating a ConvNet to classify CIFAR-10 -- Getting ready -- How to do it... -- How it works... -- There's more... -- Transferring style with VGG19 for image repainting -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using a pretrained VGG16 net for transfer learning -- Getting ready -- How to do it... -- How it works... -- There's more... -- Creating a DeepDream network -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Chapter 5: Advanced Convolutional Neural Networks -- Introduction -- Creating a ConvNet for Sentiment Analysis -- Getting ready -- How to do it... -- How it works... -- There is more... -- Inspecting what filters a VGG pre-built network has learned -- Getting ready -- How to do it... -- How it works... -- There is more. Classifying images with VGGNet, ResNet, Inception, and Xception -- VGG16 and VGG19 -- ResNet -- Inception -- Xception -- Getting ready -- How to do it... -- How it works... -- There is more... -- Recycling pre-built Deep Learning models for extracting features -- Getting ready -- How to do it... -- How it works... -- Very deep InceptionV3 Net used for Transfer Learning -- Getting ready -- How to do it... -- How it works... -- There is more... -- Generating music with dilated ConvNets, WaveNet, and NSynth -- Getting ready -- How to do it... -- How it works... -- There is more... -- Answering questions about images (Visual Q& -- A) -- How to do it... -- How it works... -- There is more... -- Classifying videos with pre-trained nets in six different ways -- How to do it... -- How it works... -- There is more... -- Chapter 6: Recurrent Neural Networks -- Introduction -- Vanishing and exploding gradients -- Long Short Term Memory (LSTM) -- Gated Recurrent Units (GRUs) and Peephole LSTM -- Operating on sequences of vectors -- Neural machine translation -- training a seq2seq RNN -- Getting ready -- How to do it... -- How it works... -- Neural machine translation -- inference on a seq2seq RNN -- How to do it... -- How it works... -- All you need is attention -- another example of a seq2seq RNN -- How to do it... -- How it works... -- There's more... -- Learning to write as Shakespeare with RNNs -- How to do it... -- How it works... -- First iteration -- After a few iterations -- There's more... -- Learning to predict future Bitcoin value with RNNs -- How to do it... -- How it works... -- There's more... -- Many-to-one and many-to-many RNN examples -- How to do it... -- How it works... -- Chapter 7: Unsupervised Learning -- Introduction -- Principal component analysis -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also. K-means clustering -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Self-organizing maps -- Getting ready -- How to do it... -- How it works... -- See also -- Restricted Boltzmann Machine -- Getting ready -- How to do it... -- How it works... -- See also -- Recommender system using RBM -- Getting ready -- How to do it... -- There's more... -- DBN for Emotion Detection -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 8: Autoencoders -- Introduction -- See Also -- Vanilla autoencoders -- Getting ready -- How to do it... -- How it works... -- There's more... -- Sparse autoencoder -- Getting Ready... -- How to do it... -- How it works... -- There's More... -- See Also -- Denoising autoencoder -- Getting Ready -- How to do it... -- See Also -- Convolutional autoencoders -- Getting Ready... -- How to do it... -- How it Works... -- There's More... -- See Also -- Stacked autoencoder -- Getting Ready -- How to do it... -- How it works... -- There's More... -- See Also -- Chapter 9: Reinforcement Learning -- Introduction -- Learning OpenAI Gym -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Implementing neural network agent to play Pac-Man -- Getting ready -- How to do it... -- Q learning to balance Cart-Pole -- Getting ready -- How to do it... -- There's more... -- See also -- Game of Atari using Deep Q Networks -- Getting ready -- How to do it... -- There's more... -- See also -- Policy gradients to play the game of Pong -- Getting ready -- How to do it... -- How it works... -- There's more... -- AlphaGo Zero -- See also -- Chapter 10: Mobile Computation -- Introduction -- TensorFlow, mobile, and the cloud -- Installing TensorFlow mobile for macOS and Android -- Getting ready -- How to do it... -- How it works... -- There's more. Playing with TensorFlow and Android examples -- Getting ready -- How to do it... -- How it works... -- Installing TensorFlow mobile for macOS and iPhone -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizing a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Profiling a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Transforming a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Chapter 11: Generative Models and CapsNet -- Introduction -- So what is a GAN? -- Some cool GAN applications -- Learning to forge MNIST images with simple GANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge MNIST images with DCGANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge Celebrity Faces and other datasets with DCGAN -- Getting ready -- How to do it... -- How it works... -- There's more... -- Implementing Variational Autoencoders -- Getting ready... -- How to do it... -- How it works... -- There's More... -- See also... -- Learning to beat the previous MNIST state-of-the-art results with Capsule Networks -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 12: Distributed TensorFlow and Cloud Deep Learning -- Introduction -- Working with TensorFlow and GPUs -- Getting ready -- How to do it... -- How it works... -- Playing with Distributed TensorFlow: multiple GPUs and one CPU -- Getting ready -- How to do it... -- How it works... -- Playing with Distributed TensorFlow: multiple servers -- Getting ready -- How to do it... -- How it works... -- There is more... -- Training a Distributed TensorFlow MNIST classifier -- Getting ready -- How to do it... -- How it works... -- Working with TensorFlow Serving and Docker. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat COMPUTERS Intelligence (AI) & Semantics. bisacsh COMPUTERS Programming Languages Python. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1661970 Volltext |
| spellingShingle | Gulli, Antonio TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python / Cover -- Copyright -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Customer Feedback -- Dedication -- Table of Contents -- Preface -- Chapter 1: TensorFlow -- An Introduction -- Introduction -- Installing TensorFlow -- Getting ready -- How to do it... -- How it works... -- There's more... -- Hello world in TensorFlow -- How to do it... -- How it works... -- Understanding the TensorFlow program structure -- How to do it... -- How it works... -- There's more... -- Working with constants, variables, and placeholders -- How to do it... -- How it works... -- There's more... -- Performing matrix manipulations using TensorFlow -- How to do it... -- How it works... -- There's more... -- Using a data flow graph -- How to do it... -- Migrating from 0.x to 1.x -- How to do it... -- There's more... -- Using XLA to enhance computational performance -- Getting ready -- How to do it... -- Invoking CPU/GPU devices -- How to do it... -- How it works... -- TensorFlow for Deep Learning -- How to do it... -- There's more -- Different Python packages required for DNN-based problems -- How to do it... -- See also -- Chapter 2: Regression -- Introduction -- Choosing loss functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizers in TensorFlow -- Getting ready -- How to do it... -- There's more... -- See also -- Reading from CSV files and preprocessing data -- Getting ready -- How to do it... -- There's more... -- House price estimation-simple linear regression -- Getting ready -- How to do it... -- How it works... -- There's more... -- House price estimation-multiple linear regression -- How to do it... -- How it works... -- There's more... -- Logistic regression on the MNIST dataset -- How to do it... -- How it works... -- See also -- Chapter 3: Neural Networks -- Perceptron -- Introduction. Activation functions -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Single layer perceptron -- Getting ready -- How to do it... -- There's more... -- Calculating gradients of backpropagation algorithm -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- MNIST classifier using MLP -- Getting ready -- How to do it... -- How it works... -- Function approximation using MLP-predicting Boston house prices -- Getting ready -- How to do it... -- How it works... -- There's more... -- Tuning hyperparameters -- How to do it... -- There's more... -- See also -- Higher-level APIs-Keras -- How to do it... -- There's more... -- See also -- Chapter 4: Convolutional Neural Networks -- Introduction -- Local receptive fields -- Shared weights and bias -- A mathematical example -- ConvNets in TensorFlow -- Pooling layers -- Max pooling -- Average pooling -- ConvNets summary -- Creating a ConvNet to classify handwritten MNIST numbers -- Getting ready -- How to do it... -- How it works... -- Creating a ConvNet to classify CIFAR-10 -- Getting ready -- How to do it... -- How it works... -- There's more... -- Transferring style with VGG19 for image repainting -- Getting ready -- How to do it... -- How it works... -- There's more... -- Using a pretrained VGG16 net for transfer learning -- Getting ready -- How to do it... -- How it works... -- There's more... -- Creating a DeepDream network -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Chapter 5: Advanced Convolutional Neural Networks -- Introduction -- Creating a ConvNet for Sentiment Analysis -- Getting ready -- How to do it... -- How it works... -- There is more... -- Inspecting what filters a VGG pre-built network has learned -- Getting ready -- How to do it... -- How it works... -- There is more. Classifying images with VGGNet, ResNet, Inception, and Xception -- VGG16 and VGG19 -- ResNet -- Inception -- Xception -- Getting ready -- How to do it... -- How it works... -- There is more... -- Recycling pre-built Deep Learning models for extracting features -- Getting ready -- How to do it... -- How it works... -- Very deep InceptionV3 Net used for Transfer Learning -- Getting ready -- How to do it... -- How it works... -- There is more... -- Generating music with dilated ConvNets, WaveNet, and NSynth -- Getting ready -- How to do it... -- How it works... -- There is more... -- Answering questions about images (Visual Q& -- A) -- How to do it... -- How it works... -- There is more... -- Classifying videos with pre-trained nets in six different ways -- How to do it... -- How it works... -- There is more... -- Chapter 6: Recurrent Neural Networks -- Introduction -- Vanishing and exploding gradients -- Long Short Term Memory (LSTM) -- Gated Recurrent Units (GRUs) and Peephole LSTM -- Operating on sequences of vectors -- Neural machine translation -- training a seq2seq RNN -- Getting ready -- How to do it... -- How it works... -- Neural machine translation -- inference on a seq2seq RNN -- How to do it... -- How it works... -- All you need is attention -- another example of a seq2seq RNN -- How to do it... -- How it works... -- There's more... -- Learning to write as Shakespeare with RNNs -- How to do it... -- How it works... -- First iteration -- After a few iterations -- There's more... -- Learning to predict future Bitcoin value with RNNs -- How to do it... -- How it works... -- There's more... -- Many-to-one and many-to-many RNN examples -- How to do it... -- How it works... -- Chapter 7: Unsupervised Learning -- Introduction -- Principal component analysis -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also. K-means clustering -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Self-organizing maps -- Getting ready -- How to do it... -- How it works... -- See also -- Restricted Boltzmann Machine -- Getting ready -- How to do it... -- How it works... -- See also -- Recommender system using RBM -- Getting ready -- How to do it... -- There's more... -- DBN for Emotion Detection -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 8: Autoencoders -- Introduction -- See Also -- Vanilla autoencoders -- Getting ready -- How to do it... -- How it works... -- There's more... -- Sparse autoencoder -- Getting Ready... -- How to do it... -- How it works... -- There's More... -- See Also -- Denoising autoencoder -- Getting Ready -- How to do it... -- See Also -- Convolutional autoencoders -- Getting Ready... -- How to do it... -- How it Works... -- There's More... -- See Also -- Stacked autoencoder -- Getting Ready -- How to do it... -- How it works... -- There's More... -- See Also -- Chapter 9: Reinforcement Learning -- Introduction -- Learning OpenAI Gym -- Getting ready -- How to do it... -- How it works... -- There's more... -- See also -- Implementing neural network agent to play Pac-Man -- Getting ready -- How to do it... -- Q learning to balance Cart-Pole -- Getting ready -- How to do it... -- There's more... -- See also -- Game of Atari using Deep Q Networks -- Getting ready -- How to do it... -- There's more... -- See also -- Policy gradients to play the game of Pong -- Getting ready -- How to do it... -- How it works... -- There's more... -- AlphaGo Zero -- See also -- Chapter 10: Mobile Computation -- Introduction -- TensorFlow, mobile, and the cloud -- Installing TensorFlow mobile for macOS and Android -- Getting ready -- How to do it... -- How it works... -- There's more. Playing with TensorFlow and Android examples -- Getting ready -- How to do it... -- How it works... -- Installing TensorFlow mobile for macOS and iPhone -- Getting ready -- How to do it... -- How it works... -- There's more... -- Optimizing a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Profiling a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Transforming a TensorFlow graph for mobile devices -- Getting ready -- How to do it... -- How it works... -- Chapter 11: Generative Models and CapsNet -- Introduction -- So what is a GAN? -- Some cool GAN applications -- Learning to forge MNIST images with simple GANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge MNIST images with DCGANs -- Getting ready -- How to do it... -- How it works... -- Learning to forge Celebrity Faces and other datasets with DCGAN -- Getting ready -- How to do it... -- How it works... -- There's more... -- Implementing Variational Autoencoders -- Getting ready... -- How to do it... -- How it works... -- There's More... -- See also... -- Learning to beat the previous MNIST state-of-the-art results with Capsule Networks -- Getting ready -- How to do it... -- How it works... -- There's more... -- Chapter 12: Distributed TensorFlow and Cloud Deep Learning -- Introduction -- Working with TensorFlow and GPUs -- Getting ready -- How to do it... -- How it works... -- Playing with Distributed TensorFlow: multiple GPUs and one CPU -- Getting ready -- How to do it... -- How it works... -- Playing with Distributed TensorFlow: multiple servers -- Getting ready -- How to do it... -- How it works... -- There is more... -- Training a Distributed TensorFlow MNIST classifier -- Getting ready -- How to do it... -- How it works... -- Working with TensorFlow Serving and Docker. Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat COMPUTERS Intelligence (AI) & Semantics. bisacsh COMPUTERS Programming Languages Python. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh96008834 https://id.nlm.nih.gov/mesh/D001185 https://id.nlm.nih.gov/mesh/D000069550 |
| title | TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python / |
| title_auth | TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python / |
| title_exact_search | TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python / |
| title_full | TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python / Antonio Gulli, Amita Kapoor. |
| title_fullStr | TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python / Antonio Gulli, Amita Kapoor. |
| title_full_unstemmed | TensorFlow 1.x deep learning cookbook : over 90 unique recipes to solve artificial-intelligence driven problems with Python / Antonio Gulli, Amita Kapoor. |
| title_short | TensorFlow 1.x deep learning cookbook : |
| title_sort | tensorflow 1 x deep learning cookbook over 90 unique recipes to solve artificial intelligence driven problems with python |
| title_sub | over 90 unique recipes to solve artificial-intelligence driven problems with Python / |
| topic | Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Artificial Intelligence https://id.nlm.nih.gov/mesh/D001185 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. aat COMPUTERS Intelligence (AI) & Semantics. bisacsh COMPUTERS Programming Languages Python. bisacsh Artificial intelligence fast Machine learning fast Python (Computer program language) fast |
| topic_facet | Machine learning. Artificial intelligence. Python (Computer program language) Artificial Intelligence Machine Learning Apprentissage automatique. Intelligence artificielle. Python (Langage de programmation) artificial intelligence. COMPUTERS Intelligence (AI) & Semantics. COMPUTERS Programming Languages Python. Artificial intelligence Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=1661970 |
| work_keys_str_mv | AT gulliantonio tensorflow1xdeeplearningcookbookover90uniquerecipestosolveartificialintelligencedrivenproblemswithpython |