Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x
About This Book
- Skill up and implement tricky neural networks using Google's TensorFlow 1.x
- An easy-to-follow guide that lets you explore reinforcement learning, GANs, autoencoders, multilayer perceptrons and more.
- Hands-on recipes to work with Tensorflow on desktop, mobile, and cloud environment
Who This Book Is For
This book is intended for data analysts, data scientists, machine learning practitioners and deep learning enthusiasts who want to perform deep learning tasks on a regular basis and are looking for a handy guide they can refer to. People who are slightly familiar with neural networks, and now want to gain expertise in working with different types of neural networks and datasets, will find this book quite useful.
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.
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.
Style and approach
This book consists of hands-on recipes where you'll deal with real-world problems.
You'll execute a series of tasks as you walk through data mining challenges using TensorFlow 1.x.
Your one-stop solution for common and not-so-common pain points, this is a book that you must have on the shelf.
Table of contents
TensorFlow - An 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
- 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
Neural Networks - Perceptron
- 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
Convolutional Neural Networks
- 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
Advanced Convolutional Neural Networks
- 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
- Answering questions about images (Visual Q&A)
- Classifying videos with pre-trained nets in six different ways
Recurrent Neural Networks
- 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
- Principal component analysis
- k-means clustering
- Self-organizing maps
- Restricted Boltzmann Machine
- Recommender system using RBM
- DBN for Emotion Detection
- Vanilla autoencoders
- Sparse autoencoder
- Denoising autoencoder
- Convolutional autoencoders
- Stacked autoencoder
- 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
- 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
Generative Models and CapsNet
- 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
Distributed TensorFlow and Cloud Deep Learning
- 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
- Learning to Learn with AutoML (Meta-Learning)
- TensorFlow Processing Units
- Title: TensorFlow 1.x Deep Learning Cookbook
- Release date: December 2017
- Publisher(s): Packt Publishing
- ISBN: 9781788293594
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