Mastering TensorFlow 1.x

Build, scale, and deploy deep neural network models using the star libraries in Python
Preview in Mapt

Mastering TensorFlow 1.x

Armando Fandango

1 customer reviews
Build, scale, and deploy deep neural network models using the star libraries in Python

Quick links: > What will you learn?> Table of content> Product reviews

Mapt Subscription
FREE
$29.99/m after trial
eBook
$19.60
RRP $27.99
Save 29%
Print + eBook
$34.99
RRP $34.99
What do I get with a Mapt Pro subscription?
  • Unlimited access to all Packt’s 5,000+ eBooks and Videos
  • Early Access content, Progress Tracking, and Assessments
  • 1 Free eBook or Video to download and keep every month after trial
What do I get with an eBook?
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with Print & eBook?
  • Get a paperback copy of the book delivered to you
  • Download this book in EPUB, PDF, MOBI formats
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
What do I get with a Video?
  • Download this Video course in MP4 format
  • DRM FREE - read and interact with your content when you want, where you want, and how you want
  • Access this title in the Mapt reader
$0.00
$19.60
$34.99
$29.99 p/m after trial
RRP $27.99
RRP $34.99
Subscription
eBook
Print + eBook
Start 14 Day Trial

Frequently bought together


Mastering TensorFlow 1.x Book Cover
Mastering TensorFlow 1.x
$ 27.99
$ 19.60
TensorFlow 1.x Deep Learning Cookbook Book Cover
TensorFlow 1.x Deep Learning Cookbook
$ 35.99
$ 25.20
Buy 2 for $35.00
Save $28.98
Add to Cart

Book Details

ISBN 139781788292061
Paperback474 pages

Book Description

TensorFlow is the most popular numerical computation library built from the ground up for distributed, cloud, and mobile environments. TensorFlow represents the data as tensors and the computation as graphs.

This book is a comprehensive guide that lets you explore the advanced features of TensorFlow 1.x. Gain insight into TensorFlow Core, Keras, TF Estimators, TFLearn, TF Slim, Pretty Tensor, and Sonnet. Leverage the power of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Throughout the book, you will obtain hands-on experience with varied datasets, such as MNIST, CIFAR-10, PTB, text8, and COCO-Images.

You will learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF Clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You will see how to call TensorFlow and Keras API within the R statistical software, and learn the required techniques for debugging when the TensorFlow API-based code does not work as expected.

The book helps you obtain in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems. By the end of this guide, you will have mastered the offerings of TensorFlow and Keras, and gained the skills you need to build smarter, faster, and efficient machine learning and deep learning systems.

Table of Contents

Chapter 1: TensorFlow 101
What is TensorFlow?
TensorFlow core
Data flow graph or computation graph
TensorBoard
Summary
Chapter 2: High-Level Libraries for TensorFlow
TF Estimator - previously TF Learn
TF Slim
TFLearn
PrettyTensor
Sonnet
Summary
Chapter 3: Keras 101
Installing Keras
Neural Network Models in Keras
Creating the Keras model
Keras Layers
Adding 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
Multi-regression
Regularized regression
Classification using logistic regression
Binary classification
Multiclass classification
Summary
Chapter 5: Neural Networks and MLP with TensorFlow and Keras
The perceptron
MultiLayer Perceptron
MLP for image classification
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
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
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
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
Summary
Chapter 9: CNN with TensorFlow and Keras
Understanding convolution
Understanding pooling
CNN architecture pattern - LeNet
LeNet for MNIST data
LeNet for CIFAR10 Data
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 Keras models
TensorFlow Serving
TF Serving in the Docker containers
TensorFlow Serving on 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 preprocessing in TensorFlow for pre-trained VGG16
VGG16 in Keras
Inception v3 in TensorFlow
Summary
Chapter 13: Deep Reinforcement Learning
OpenAI Gym 101
Applying simple policies to a cartpole game
Reinforcement learning 101
Naive Neural Network policy for Reinforcement Learning
Implementing Q-Learning
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
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
Chapter 19: Tensor Processing Units

What You Will Learn

  • Master advanced concepts of deep learning such as transfer learning, reinforcement learning, generative models and more, using TensorFlow and Keras
  • Perform supervised (classification and regression) and unsupervised (clustering) learning to solve machine learning tasks
  • Build end-to-end deep learning (CNN, RNN, and Autoencoders) models with TensorFlow
  • Scale and deploy production models with distributed and high-performance computing on GPU and clusters
  • Build TensorFlow models to work with multilayer perceptrons using Keras, TFLearn, and R
  • Learn the functionalities of smart apps by building and deploying TensorFlow models on iOS and Android devices
  • Supercharge TensorFlow with distributed training and deployment on Kubernetes and TensorFlow Clusters

Authors

Table of Contents

Chapter 1: TensorFlow 101
What is TensorFlow?
TensorFlow core
Data flow graph or computation graph
TensorBoard
Summary
Chapter 2: High-Level Libraries for TensorFlow
TF Estimator - previously TF Learn
TF Slim
TFLearn
PrettyTensor
Sonnet
Summary
Chapter 3: Keras 101
Installing Keras
Neural Network Models in Keras
Creating the Keras model
Keras Layers
Adding 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
Multi-regression
Regularized regression
Classification using logistic regression
Binary classification
Multiclass classification
Summary
Chapter 5: Neural Networks and MLP with TensorFlow and Keras
The perceptron
MultiLayer Perceptron
MLP for image classification
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
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
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
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
Summary
Chapter 9: CNN with TensorFlow and Keras
Understanding convolution
Understanding pooling
CNN architecture pattern - LeNet
LeNet for MNIST data
LeNet for CIFAR10 Data
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 Keras models
TensorFlow Serving
TF Serving in the Docker containers
TensorFlow Serving on 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 preprocessing in TensorFlow for pre-trained VGG16
VGG16 in Keras
Inception v3 in TensorFlow
Summary
Chapter 13: Deep Reinforcement Learning
OpenAI Gym 101
Applying simple policies to a cartpole game
Reinforcement learning 101
Naive Neural Network policy for Reinforcement Learning
Implementing Q-Learning
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
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
Chapter 19: Tensor Processing Units

Book Details

ISBN 139781788292061
Paperback474 pages
Read More
From 1 reviews

Read More Reviews

Recommended for You

TensorFlow 1.x Deep Learning Cookbook Book Cover
TensorFlow 1.x Deep Learning Cookbook
$ 35.99
$ 25.20
Predictive Analytics with TensorFlow Book Cover
Predictive Analytics with TensorFlow
$ 39.99
$ 28.00
Deep Learning By Example Book Cover
Deep Learning By Example
$ 39.99
$ 28.00
Mastering Machine Learning with scikit-learn - Second Edition Book Cover
Mastering Machine Learning with scikit-learn - Second Edition
$ 35.99
$ 25.20
Machine Learning with TensorFlow 1.x Book Cover
Machine Learning with TensorFlow 1.x
$ 31.99
$ 22.40
The Complete Guide to TensorFlow 1.x Book Cover
The Complete Guide to TensorFlow 1.x
$ 124.99
$ 106.25