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• Leave a review - let other readers know what you think
• Other Books You May Enjoy
• Tensor Processing Units
• Summary
• Debugging with the TensorFlow debugger (tfdbg)
• Asserting on conditions with tf.Assert()

• Leave a review - let other readers know what you think 更新時間：2021-06-25 22:51:43
• Other Books You May Enjoy
• Tensor Processing Units
• Summary
• Debugging with the TensorFlow debugger (tfdbg)
• Asserting on conditions with tf.Assert()
• Printing tensor values with tf.Print()
• Fetching tensor values with tf.Session.run()
• Debugging TensorFlow Models
• Summary
• The tfruns package in R
• TensorBoard in R
• Keras API in R
• TF estimator API in R
• TF core API in R
• Installing TensorFlow and Keras packages in R
• TensorFlow and Keras in R
• Summary
• TF Lite demo on iOS
• TF Lite Demo on Android
• TensorFlow Lite
• TF Mobile demo on iOS
• TF Mobile in iOS apps
• TF Mobile demo on Android
• TF Mobile in Android apps
• TensorFlow on mobile platforms
• TensorFlow Models on Mobile and Embedded Platforms
• Summary
• Define and train the graph for synchronous updates
• Define and train the graph for asynchronous updates
• Define the parameter and operations across servers and devices
• Create the server instances
• Defining cluster specification
• TensorFlow clusters
• Strategies for distributed execution
• Distributed Models with TensorFlow Clusters
• Summary
• Deep Convolutional GAN with TensorFlow and Keras
• Simple GAN with Keras
• Simple GAN with TensorFlow
• Best practices for building and training GANs
• Generative Adversarial Networks 101
• Generative Adversarial Networks
• Summary
• Q-Learning with Q-Network or Deep Q Network (DQN)
• Q-Learning with Q-Table
• Initializing and discretizing for Q-Learning
• Implementing Q-Learning
• Naive Neural Network policy for Reinforcement Learning
• Reinforcement learning techniques
• V function (learning to optimize when the model is available)
• Exploration and exploitation in the RL algorithms
• Q function (learning to optimize when the model is not available)
• Reinforcement learning 101
• Applying simple policies to a cartpole game
• OpenAI Gym 101
• Deep Reinforcement Learning
• Summary
• Image classification using retrained Inception v3 in TensorFlow
• Image classification using Inception v3 in TensorFlow
• Inception v3 in TensorFlow
• Image classification using retrained VGG16 in Keras
• Image classification using pre-trained VGG16 in Keras
• VGG16 in Keras
• Image classification using retrained VGG16 in TensorFlow
• Image preprocessing in TensorFlow for pre-trained VGG16
• Image classification using pre-trained VGG16 in TensorFlow
• VGG16 in TensorFlow
• COCO animals dataset and pre-processing images
• Retraining or fine-tuning models
• ImageNet dataset
• Transfer Learning and Pre-Trained Models
• Summary
• Deploying in Kubernetes
• Uploading the Docker image to the dockerhub
• Installing Kubernetes
• TensorFlow Serving on Kubernetes
• Serving the model in the Docker container
• Building a Docker image for TF serving
• Installing Docker
• TF Serving in the Docker containers
• Serving models with TF Serving
• Saving models for TF Serving
• Installing TF Serving
• TensorFlow Serving
• Saving and restoring Keras models
• Saving and restoring selected variables with the saver class
• Saving and restoring all graph variables with the saver class
• Saving and Restoring models in TensorFlow
• TensorFlow Models in Production with TF Serving
• Summary
• Variational autoencoder in Keras
• Variational autoencoder in TensorFlow
• Denoising autoencoder in Keras
• Denoising autoencoder in TensorFlow
• Stacked autoencoder in Keras
• Stacked autoencoder in TensorFlow
• Autoencoder types
• Autoencoder with TensorFlow and Keras
• Summary
• ConvNets for CIFAR10 with Keras
• ConvNets for CIFAR10 with TensorFlow
• LeNet for CIFAR10 Data
• LeNet CNN for MNIST with Keras
• LeNet CNN for MNIST with TensorFlow
• LeNet for MNIST data
• CNN architecture pattern - LeNet
• Understanding pooling
• Understanding convolution
• CNN with TensorFlow and Keras
• Summary
• Text generation LSTM in Keras
• Text generation LSTM in TensorFlow
• Text generation with RNN models in TensorFlow and Keras
• skip-gram model with Keras
• Visualize the word embeddings using t-SNE
• skip-gram model with TensorFlow
• Preparing the small validation set
• Loading and preparing the text8 dataset
• Loading and preparing the PTB dataset
• Preparing the data for word2vec models
• Word vector representations
• RNN for Text Data with TensorFlow and Keras
• Summary
• GRU with Keras
• LSTM with Keras
• Simple RNN with Keras
• Preprocessing the dataset for RNN models with Keras
• GRU in TensorFlow
• LSTM in TensorFlow
• Simple RNN in TensorFlow
• Preprocessing the dataset for RNN models with TensorFlow
• Visualizing the airpass dataset
• Loading the airpass dataset
• Airline Passengers dataset
• RNN for Time Series Data with TensorFlow and Keras
• Summary
• RNN in Keras for MNIST data
• Application areas of RNNs
• Keras for RNN
• TensorFlow RNN Cell Wrapper Classes
• TensorFlow RNN Model Construction Classes
• TensorFlow RNN Cell Classes
• TensorFlow for RNN
• GRU network
• LSTM network
• RNN variants
• Simple Recurrent Neural Network
• RNN with TensorFlow and Keras
• Summary
• MLP for time series regression
• Summary of MLP with TensorFlow Keras and TFLearn
• TFLearn-based MLP for MNIST classification
• Keras-based MLP for MNIST classification
• TensorFlow-based MLP for MNIST classification
• MLP for image classification
• MultiLayer Perceptron
• The perceptron
• Neural Networks and MLP with TensorFlow and Keras
• Summary
• Multiclass classification
• Binary classification
• Logistic regression for multiclass classification
• Logistic regression for binary classification
• Classification using logistic regression
• ElasticNet regularization
• Ridge regularization
• Lasso regularization
• Regularized regression
• Multi-regression
• Using the trained model to predict
• Training the model
• Defining the optimizer function
• Defining the loss function
• Defining the model
• Defining the inputs parameters and other variables
• Building a simple regression model
• Data preparation
• Simple linear regression
• Classical Machine Learning with TensorFlow
• Summary
• Keras sequential model example for MNIST dataset
• Additional modules in Keras
• Predicting with the Keras model
• Training the Keras model
• Compiling the Keras model
• Functional API to add layers to the Keras Model
• Sequential API to add layers to the Keras model
• Adding Layers to the Keras Model
• Keras noise layers
• Keras normalization layers
• Keras advanced activation layers
• Keras merge layers
• Keras embedding layers
• Keras recurrent layers
• Keras locally-connected layers
• Keras pooling layers
• Keras convolutional layers
• Keras core layers
• Keras Layers
• Functional API for creating the Keras model
• Sequential API for creating the Keras model
• Creating the Keras model
• Workflow for building models in Keras
• Neural Network Models in Keras
• Installing Keras
• Keras 101
• Summary
• Sonnet
• PrettyTensor
• Using the TFLearn Model
• Training the TFLearn Model
• Types of TFLearn models
• Creating the TFLearn Model
• TFLearn estimator layers
• TFLearn merge layers
• TFLearn embedding layers
• TFLearn normalization layers
• TFLearn recurrent layers
• TFLearn convolutional layers
• TFLearn core layers
• Creating the TFLearn Layers
• TFLearn
• TF Slim
• TF Estimator - previously TF Learn
• High-Level Libraries for TensorFlow
• Summary
• TensorBoard details
• A TensorBoard minimal example
• TensorBoard
• Multiple graphs
• GPU memory handling
• Soft placement
• Dynamic placement
• Simple placement
• Placing graph nodes on specific compute devices
• Executing graphs across compute devices - CPU and GPGPU
• Order of execution and lazy loading
• Data flow graph or computation graph
• Getting Variables with tf.get_variable()
• Populating tensor elements with a random distribution
• Populating tensor elements with sequences
• Populating tensor elements with the same values
• Tensors generated from library functions
• Variables
• Creating tensors from Python objects
• Placeholders
• Operations
• Constants
• Tensors
• Code warm-up - Hello TensorFlow
• TensorFlow core
• What is TensorFlow?
• TensorFlow 101
• Reviews
• Get in touch
• Conventions used
• Download the example code files
• To get the most out of this book
• What this book covers
• Who this book is for
• Preface
• Packt is searching for authors like you
• About the reviewer
• About the author
• Contributors
• Foreword
• PacktPub.com
• Why subscribe?
• Packt Upsell
• 版權信息
• 封面

• 封面
• 版權信息
• Packt Upsell
• Why subscribe?
• PacktPub.com
• Foreword
• Contributors
• About the author
• About the reviewer
• Packt is searching for authors like you
• Preface
• Who this book is for
• What this book covers
• To get the most out of this book
• Download the example code files
• Conventions used
• Get in touch
• Reviews
• TensorFlow 101
• What is TensorFlow?
• TensorFlow core
• Code warm-up - Hello TensorFlow
• Tensors
• Constants
• Operations
• Placeholders
• Creating tensors from Python objects
• Variables
• Tensors generated from library functions
• Populating tensor elements with the same values
• Populating tensor elements with sequences
• Populating tensor elements with a random distribution
• Getting Variables with tf.get_variable()
• Data flow graph or computation graph
• Order of execution and lazy loading
• Executing graphs across compute devices - CPU and GPGPU
• Placing graph nodes on specific compute devices
• Simple placement
• Dynamic placement
• Soft placement
• GPU memory handling
• Multiple graphs
• TensorBoard
• A TensorBoard minimal example
• TensorBoard details
• Summary
• High-Level Libraries for TensorFlow
• TF Estimator - previously TF Learn
• TF Slim
• TFLearn
• Creating the TFLearn Layers
• TFLearn core layers
• TFLearn convolutional layers
• TFLearn recurrent layers
• TFLearn normalization layers
• TFLearn embedding layers
• TFLearn merge layers
• TFLearn estimator layers
• Creating the TFLearn Model
• Types of TFLearn models
• Training the TFLearn Model
• Using the TFLearn Model
• PrettyTensor
• Sonnet
• Summary
• Keras 101
• Installing Keras
• Neural Network Models in Keras
• Workflow for building models in Keras
• Creating the Keras model
• Sequential API for creating the Keras model
• Functional API for creating the Keras model
• Keras Layers
• Keras core layers
• Keras convolutional layers
• Keras pooling layers
• Keras locally-connected layers
• Keras recurrent layers
• Keras embedding layers
• Keras merge layers
• Keras advanced activation layers
• Keras normalization layers
• Keras noise layers
• Adding Layers to the Keras Model
• Sequential API to add layers to the Keras model
• Functional API to add layers to the Keras Model
• Compiling the Keras model
• Training the Keras model
• Predicting with the Keras model
• Additional modules in Keras
• Keras sequential model example for MNIST dataset
• Summary
• Classical Machine Learning with TensorFlow
• Simple linear regression
• Data preparation
• Building a simple regression model
• Defining the inputs parameters and other variables
• Defining the model
• Defining the loss function
• Defining the optimizer function
• Training the model
• Using the trained model to predict
• Multi-regression
• Regularized regression
• Lasso regularization
• Ridge regularization
• ElasticNet regularization
• Classification using logistic regression
• Logistic regression for binary classification
• Logistic regression for multiclass classification
• Binary classification
• Multiclass classification
• Summary
• Neural Networks and MLP with TensorFlow and Keras
• The perceptron
• MultiLayer Perceptron
• MLP for image classification
• TensorFlow-based MLP for MNIST classification
• Keras-based MLP for MNIST classification
• TFLearn-based MLP for MNIST classification
• Summary of MLP with TensorFlow Keras and TFLearn
• MLP for time series regression
• Summary
• RNN with TensorFlow and Keras
• Simple Recurrent Neural Network
• RNN variants
• LSTM network
• GRU network
• TensorFlow for RNN
• TensorFlow RNN Cell Classes
• TensorFlow RNN Model Construction Classes
• TensorFlow RNN Cell Wrapper Classes
• Keras for RNN
• Application areas of RNNs
• RNN in Keras for MNIST data
• Summary
• RNN for Time Series Data with TensorFlow and Keras
• Airline Passengers dataset
• Loading the airpass dataset
• Visualizing the airpass dataset
• Preprocessing the dataset for RNN models with TensorFlow
• Simple RNN in TensorFlow
• LSTM in TensorFlow
• GRU in TensorFlow
• Preprocessing the dataset for RNN models with Keras
• Simple RNN with Keras
• LSTM with Keras
• GRU with Keras
• Summary
• RNN for Text Data with TensorFlow and Keras
• Word vector representations
• Preparing the data for word2vec models
• Loading and preparing the PTB dataset
• Loading and preparing the text8 dataset
• Preparing the small validation set
• skip-gram model with TensorFlow
• Visualize the word embeddings using t-SNE
• skip-gram model with Keras
• Text generation with RNN models in TensorFlow and Keras
• Text generation LSTM in TensorFlow
• Text generation LSTM in Keras
• Summary
• CNN with TensorFlow and Keras
• Understanding convolution
• Understanding pooling
• CNN architecture pattern - LeNet
• LeNet for MNIST data
• LeNet CNN for MNIST with TensorFlow
• LeNet CNN for MNIST with Keras
• LeNet for CIFAR10 Data
• ConvNets for CIFAR10 with TensorFlow
• ConvNets for CIFAR10 with Keras
• Summary
• Autoencoder with TensorFlow and Keras
• Autoencoder types
• Stacked autoencoder in TensorFlow
• Stacked autoencoder in Keras
• Denoising autoencoder in TensorFlow
• Denoising autoencoder in Keras
• Variational autoencoder in TensorFlow
• Variational autoencoder in Keras
• Summary
• TensorFlow Models in Production with TF Serving
• Saving and Restoring models in TensorFlow
• Saving and restoring all graph variables with the saver class
• Saving and restoring selected variables with the saver class
• Saving and restoring Keras models
• TensorFlow Serving
• Installing TF Serving
• Saving models for TF Serving
• Serving models with TF Serving
• TF Serving in the Docker containers
• Installing Docker
• Building a Docker image for TF serving
• Serving the model in the Docker container
• TensorFlow Serving on Kubernetes
• Installing Kubernetes
• Uploading the Docker image to the dockerhub
• Deploying in Kubernetes
• Summary
• Transfer Learning and Pre-Trained Models
• ImageNet dataset
• Retraining or fine-tuning models
• COCO animals dataset and pre-processing images
• VGG16 in TensorFlow
• Image classification using pre-trained VGG16 in TensorFlow
• Image preprocessing in TensorFlow for pre-trained VGG16
• Image classification using retrained VGG16 in TensorFlow
• VGG16 in Keras
• Image classification using pre-trained VGG16 in Keras
• Image classification using retrained VGG16 in Keras
• Inception v3 in TensorFlow
• Image classification using Inception v3 in TensorFlow
• Image classification using retrained Inception v3 in TensorFlow
• Summary
• Deep Reinforcement Learning
• OpenAI Gym 101
• Applying simple policies to a cartpole game
• Reinforcement learning 101
• Q function (learning to optimize when the model is not available)
• Exploration and exploitation in the RL algorithms
• V function (learning to optimize when the model is available)
• Reinforcement learning techniques
• Naive Neural Network policy for Reinforcement Learning
• Implementing Q-Learning
• Initializing and discretizing for Q-Learning
• Q-Learning with Q-Table
• Q-Learning with Q-Network or Deep Q Network (DQN)
• Summary
• Generative Adversarial Networks
• Generative Adversarial Networks 101
• Best practices for building and training GANs
• Simple GAN with TensorFlow
• Simple GAN with Keras
• Deep Convolutional GAN with TensorFlow and Keras
• Summary
• Distributed Models with TensorFlow Clusters
• Strategies for distributed execution
• TensorFlow clusters
• Defining cluster specification
• Create the server instances
• Define the parameter and operations across servers and devices
• Define and train the graph for asynchronous updates
• Define and train the graph for synchronous updates
• Summary
• TensorFlow Models on Mobile and Embedded Platforms
• TensorFlow on mobile platforms
• TF Mobile in Android apps
• TF Mobile demo on Android
• TF Mobile in iOS apps
• TF Mobile demo on iOS
• TensorFlow Lite
• TF Lite Demo on Android
• TF Lite demo on iOS
• Summary
• TensorFlow and Keras in R
• Installing TensorFlow and Keras packages in R
• TF core API in R
• TF estimator API in R
• Keras API in R
• TensorBoard in R
• The tfruns package in R
• Summary
• Debugging TensorFlow Models
• Fetching tensor values with tf.Session.run()
• Printing tensor values with tf.Print()
• Asserting on conditions with tf.Assert()
• Debugging with the TensorFlow debugger (tfdbg)
• Summary
• Tensor Processing Units
• Other Books You May Enjoy
• Leave a review - let other readers know what you think 更新時間：2021-06-25 22:51:43