TensorFlow 1.x Deep Learning Cookbook

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x
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TensorFlow 1.x Deep Learning Cookbook

Antonio Gulli, Amita Kapoor

3 customer reviews
Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x

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Book Details

ISBN 139781788293594
Paperback536 pages

Book Description

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-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow.

With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work. You will learn the performance of different DNNs on some popularly used data sets such as MNIST, CIFAR-10, Youtube8m, and more. You will not only learn about the different mobile and embedded platforms supported by TensorFlow but also how to set up cloud platforms for deep learning applications. Get a sneak peek of TPU architecture and how they will affect DNN future.

By using crisp, no-nonsense recipes, you will become an expert in implementing deep learning techniques in growing real-world applications and research areas such as reinforcement learning, GANs, autoencoders and more.

Table of Contents

Chapter 1: TensorFlow - An Introduction
Introduction
Installing TensorFlow
Hello world in TensorFlow
Understanding the TensorFlow program structure
Working with constants, variables, and placeholders
Performing matrix manipulations using TensorFlow
Using a data flow graph
Migrating from 0.x to 1.x
Using XLA to enhance computational performance
Invoking CPU/GPU devices
TensorFlow for Deep Learning
Different Python packages required for DNN-based problems
Chapter 2: Regression
Introduction
Choosing loss functions
Optimizers in TensorFlow
Reading from CSV files and preprocessing data
House price estimation-simple linear regression
House price estimation-multiple linear regression
Logistic regression on the MNIST dataset
Chapter 3: Neural Networks - Perceptron
Introduction
Activation functions
Single layer perceptron
Calculating gradients of backpropagation algorithm 
MNIST classifier using MLP
Function approximation using MLP-predicting Boston house prices
Tuning hyperparameters
Higher-level APIs-Keras
See also
Chapter 4: Convolutional Neural Networks
Introduction
Creating a ConvNet to classify handwritten MNIST numbers
Creating a ConvNet to classify CIFAR-10
Transferring style with VGG19 for image repainting
Using a pretrained VGG16 net for transfer learning
Creating a DeepDream network
Chapter 5: Advanced Convolutional Neural Networks
Introduction
Creating a ConvNet for Sentiment Analysis
Inspecting what filters a VGG pre-built network has learned
Classifying images with VGGNet, ResNet, Inception, and Xception
Recycling pre-built Deep Learning models for extracting features
Very deep InceptionV3 Net used for Transfer Learning
Generating music with dilated ConvNets, WaveNet, and NSynth
Classifying videos with pre-trained nets in six different ways
Chapter 6: Recurrent Neural Networks
Introduction
Neural machine translation - training a seq2seq RNN
Neural machine translation - inference on a seq2seq RNN
All you need is attention - another example of a seq2seq RNN
Learning to write as Shakespeare with RNNs
Learning to predict future Bitcoin value with RNNs
Many-to-one and many-to-many RNN examples
Chapter 7: Unsupervised Learning
Introduction
Principal component analysis
k-means clustering
Self-organizing maps
Restricted Boltzmann Machine
Recommender system using RBM
DBN for Emotion Detection
Chapter 8: Autoencoders
Introduction
Vanilla autoencoders
Sparse autoencoder
Denoising autoencoder
Convolutional autoencoders
Stacked autoencoder
Chapter 9: Reinforcement Learning
Introduction
Learning OpenAI Gym
Implementing neural network agent to play Pac-Man
Q learning to balance Cart-Pole
Game of Atari using Deep Q Networks
Policy gradients to play the game of Pong
Chapter 10: Mobile Computation
Introduction
Installing TensorFlow mobile for macOS and Android
Playing with TensorFlow and Android examples
Installing TensorFlow mobile for macOS and iPhone
Optimizing a TensorFlow graph for mobile devices
Profiling a TensorFlow graph for mobile devices
Transforming a TensorFlow graph for mobile devices
Chapter 11: Generative Models and CapsNet
Introduction
Learning to forge MNIST images with simple GANs
Learning to forge MNIST images with DCGANs
Learning to forge Celebrity Faces and other datasets with DCGAN
Implementing Variational Autoencoders
Learning to beat the previous MNIST state-of-the-art results with Capsule Networks
Chapter 12: Distributed TensorFlow and Cloud Deep Learning
Introduction
Working with TensorFlow and GPUs
Playing with Distributed TensorFlow: multiple GPUs and one CPU
Playing with Distributed TensorFlow: multiple servers
Training a Distributed TensorFlow MNIST classifier
Working with TensorFlow Serving and Docker
Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine
Running Distributed TensorFlow on Google CloudML
Running Distributed TensorFlow on Microsoft Azure
Running Distributed TensorFlow on Amazon AWS
Chapter 13: Learning to Learn with AutoML (Meta-Learning)
Meta-learning with recurrent networks and with reinforcement learning
Meta-learning blocks
Meta-learning novel tasks
Siamese Network
Chapter 14: TensorFlow Processing Units
Components of TPUs

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.

Authors

Table of Contents

Chapter 1: TensorFlow - An Introduction
Introduction
Installing TensorFlow
Hello world in TensorFlow
Understanding the TensorFlow program structure
Working with constants, variables, and placeholders
Performing matrix manipulations using TensorFlow
Using a data flow graph
Migrating from 0.x to 1.x
Using XLA to enhance computational performance
Invoking CPU/GPU devices
TensorFlow for Deep Learning
Different Python packages required for DNN-based problems
Chapter 2: Regression
Introduction
Choosing loss functions
Optimizers in TensorFlow
Reading from CSV files and preprocessing data
House price estimation-simple linear regression
House price estimation-multiple linear regression
Logistic regression on the MNIST dataset
Chapter 3: Neural Networks - Perceptron
Introduction
Activation functions
Single layer perceptron
Calculating gradients of backpropagation algorithm 
MNIST classifier using MLP
Function approximation using MLP-predicting Boston house prices
Tuning hyperparameters
Higher-level APIs-Keras
See also
Chapter 4: Convolutional Neural Networks
Introduction
Creating a ConvNet to classify handwritten MNIST numbers
Creating a ConvNet to classify CIFAR-10
Transferring style with VGG19 for image repainting
Using a pretrained VGG16 net for transfer learning
Creating a DeepDream network
Chapter 5: Advanced Convolutional Neural Networks
Introduction
Creating a ConvNet for Sentiment Analysis
Inspecting what filters a VGG pre-built network has learned
Classifying images with VGGNet, ResNet, Inception, and Xception
Recycling pre-built Deep Learning models for extracting features
Very deep InceptionV3 Net used for Transfer Learning
Generating music with dilated ConvNets, WaveNet, and NSynth
Classifying videos with pre-trained nets in six different ways
Chapter 6: Recurrent Neural Networks
Introduction
Neural machine translation - training a seq2seq RNN
Neural machine translation - inference on a seq2seq RNN
All you need is attention - another example of a seq2seq RNN
Learning to write as Shakespeare with RNNs
Learning to predict future Bitcoin value with RNNs
Many-to-one and many-to-many RNN examples
Chapter 7: Unsupervised Learning
Introduction
Principal component analysis
k-means clustering
Self-organizing maps
Restricted Boltzmann Machine
Recommender system using RBM
DBN for Emotion Detection
Chapter 8: Autoencoders
Introduction
Vanilla autoencoders
Sparse autoencoder
Denoising autoencoder
Convolutional autoencoders
Stacked autoencoder
Chapter 9: Reinforcement Learning
Introduction
Learning OpenAI Gym
Implementing neural network agent to play Pac-Man
Q learning to balance Cart-Pole
Game of Atari using Deep Q Networks
Policy gradients to play the game of Pong
Chapter 10: Mobile Computation
Introduction
Installing TensorFlow mobile for macOS and Android
Playing with TensorFlow and Android examples
Installing TensorFlow mobile for macOS and iPhone
Optimizing a TensorFlow graph for mobile devices
Profiling a TensorFlow graph for mobile devices
Transforming a TensorFlow graph for mobile devices
Chapter 11: Generative Models and CapsNet
Introduction
Learning to forge MNIST images with simple GANs
Learning to forge MNIST images with DCGANs
Learning to forge Celebrity Faces and other datasets with DCGAN
Implementing Variational Autoencoders
Learning to beat the previous MNIST state-of-the-art results with Capsule Networks
Chapter 12: Distributed TensorFlow and Cloud Deep Learning
Introduction
Working with TensorFlow and GPUs
Playing with Distributed TensorFlow: multiple GPUs and one CPU
Playing with Distributed TensorFlow: multiple servers
Training a Distributed TensorFlow MNIST classifier
Working with TensorFlow Serving and Docker
Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine
Running Distributed TensorFlow on Google CloudML
Running Distributed TensorFlow on Microsoft Azure
Running Distributed TensorFlow on Amazon AWS
Chapter 13: Learning to Learn with AutoML (Meta-Learning)
Meta-learning with recurrent networks and with reinforcement learning
Meta-learning blocks
Meta-learning novel tasks
Siamese Network
Chapter 14: TensorFlow Processing Units
Components of TPUs

Book Details

ISBN 139781788293594
Paperback536 pages
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From 3 reviews

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