Fandango - Mastering TensorFlow 1.x: advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras
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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 deploy TensorFlow on mobile, and distributed TensorFlow on GPU, Clusters, and Kubernetes
Who This Book Is For
This book is for data scientists, machine learning engineers, artificial intelligence engineers, and for all TensorFlow users who wish to upgrade their TensorFlow knowledge and work on various machine learning and deep learning problems. If you are looking for an easy-to-follow guide that underlines the intricacies and complex use cases of machine learning, you will find this book extremely useful. Some basic understanding of TensorFlow is required to get the most out of the book.
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
In Detail
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.
Style and approach
Step-by-step comprehensive guide filled with advanced, real-world examples to help you master Tensorflow 1.x
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Copyright 2018 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the authors, nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.
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First published: January 2018
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ISBN 978-1-78829-206-1
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TensorFlow and Keras are a key part of the "Data Science for Internet of Things" course, which I teach at the University of Oxford. My TensorFlow journey started with Keras. Over time, in our course, we increasingly gravitated towards core TensorFlow in addition to Keras. I believe many people's 'TensorFlow journey' will follow this trajectory.
Armando Fandango's book "Mastering TensorFlow 1.x" provides a road map for this journey. The book is an ambitious undertaking, interweaving Keras and core TensorFlow libraries. It delves into complex themes and libraries such as Sonnet, distributed TensorFlow with TF Clusters, deploying production models with TensorFlow Serving, TensorFlow mobile, and TensorFlow for embedded devices.
In that sense, this is an advanced book. But the author covers deep learning models such as RNN, CNN, autoencoders, generative adversarial models, and deep reinforcement learning through Keras. Armando has clearly drawn upon his experience to make this complex journey easier for readers.
I look forward to increased adoption of this book and learning from it.
Ajit Jaokar
Data Science for IoT Course Creator and Lead Tutor at the University of Oxford / Principal Data Scientist.
ArmandoFandango creates AI-empowered products by leveraging his expertise in deep learning, computational methods, and distributed computing. He advises Owen.ai Inc on AI product strategy. He founded NeuraSights Inc. with the goal of creating insights using neural networks. He is the founder of Vets2Data Inc., a non-profit organization assisting US military veterans in building AI skills.
Armando has authored books titled Python Data Analysis - 2nd Edition and Mastering TensorFlow and published research in international journals and conferences.
I would like to thank Dr. Paul Wiegand (UCF), Dr. Brian Goldiez (UCF), Tejas Limkar (Packt), and Tushar Gupta (Packt) for being able to complete this book. This work would not be possible without their inspiration.
, TensorFlow 101 , recaps the basics of TensorFlow, such as how to create tensors, constants, variables, placeholders, and operations. We learn about computation graphs and how to place computation graph nodes on various devices such as GPU. We also learn how to use TensorBoard to visualize various intermediate and final output values.
, High-Level Libraries for TensorFLow , covers several high-level libraries such as TF Contrib Learn, TF Slim, TFLearn, Sonnet, and Pretty Tensor.
, Keras 101 , gives a detailed overview of the high-level library Keras, which is now part of the TensorFlow core.
, Classical Machine Learning with TensorFlow , teaches us to use TensorFlow to implement classical machine learning algorithms, such as linear regression and classification with logistic regression.
, Neural Networks and MLP with TensorFlow and Keras , introduces the concept of neural networks and shows how to build simple neural network models. We also cover how to build deep neural network models known as MultiLayer Perceptrons.
, RNNs with TensorFlow and Keras , covers how to build Recurrent Neural Networks with TensorFlow and Keras. We cover the internal architecture of RNN, Long Short-Term Networks (LSTM), and Gated Recurrent Units (GRU). We provide a brief overview of the API functions and classes provided by TensorFlow and Keras to implement RNN models.
, RNN for Time Series Data with TensorFlow and Keras , shows how to build and train RNN models for time series data and provide examples in TensorFlow and Keras libraries.
, RNN for Text Data with TensorFlow and Keras , teaches us how to build and train RNN models for text data and provides examples in TensorFlow and Keras libraries. We learn to build word vectors and embeddings with TensorFlow and Keras, followed by LSTM models for using embeddings to generate text from sample text data.
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