Mastering TensorFlow 1.x : advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Mastering TensorFlow 1.x advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras

Build, scale, and deploy deep neural network models using the star libraries in Python About This Book Delve into advanced machine learning and deep learning use cases using Tensorflow and Keras Build, deploy, and scale end-to-end deep neural network models in a production environment Learn to deplo...

Full description

Bibliographic Details
Other Authors: Fandango, Armando, author (author)
Format: eBook
Language:Inglés
Published: Birmingham, England : Packt Publishing 2018.
Edition:1st edition
Subjects:
Machine learning > Mathematical models.
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009631367306719
Table of Contents:
  • Cover
  • Copyright and Credits
  • Packt Upsell
  • Foreword
  • Contributors
  • Table of Contents
  • Preface
  • Chapter 1: 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
  • Chapter 2: 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
  • Chapter 3: 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
  • Chapter 4: 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
  • Chapter 5: 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
  • Chapter 6: 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
  • Chapter 7: 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
  • Chapter 8: 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
  • Chapter 9: 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
  • Chapter 10: 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
  • Chapter 11: 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
  • Chapter 12: 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
  • Chapter 13: 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
  • Chapter 14: 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.
  • Chapter 15: 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
  • Chapter 16: 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
  • Chapter 17: 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
  • Chapter 18: 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
  • Appendix: Tensor Processing Units
  • Other Books You May Enjoy
  • Index.

Similar Items