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Mastering TensorFlow 1.x
Thisbookisfordatascientists,machinelearningengineers,artificialintelligenceengineers,andforallTensorFlowuserswhowishtoupgradetheirTensorFlowknowledgeandworkonvariousmachinelearninganddeeplearningproblems.Ifyouarelookingforaneasy-to-followguidethatunderlinestheintricaciesandcomplexusecasesofmachinelearning,youwillfindthisbookextremelyuseful.SomebasicunderstandingofTensorFlowisrequiredtogetthemostoutofthebook.
目錄(272章)
倒序
- 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
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