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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
You need much more than imagination to predict earthquakes and detect brain cancer cells. Become an expert in designing and deploying TensorFlow and Keras models, and generate insightful predictions with the power of deep learning.
Key Features
- Cover the basics of neural networks and choose the right model architecture
- Make predictions with a trained model and get to grips with TensorBoard
- Evaluate metrics and techniques and deploy a model as a web application
Book Description
With this book, you’ll learn how to train, evaluate and deploy Tensorflow and Keras models as real-world web applications. After a hands-on introduction, you’ll use a sample model to explore the details of deep learning, selecting the right layers that can solve a given problem. By the end of the book, you’ll build a Bitcoin application that predicts the future price, based on historic, and freely available information.
What you will learn
- Set up a deep learning programming environment
- Explore the common components of a neural network and its essential operations
- Prepare data for a deep learning model- Deploy model as an interactive web application, with Flask and a HTTP API
- Use Keras, a TensorFlow abstraction library
- Explore the types of problems addressed by neural networks
Who this book is for
This book is ideal for experienced developers, analysts, or a data scientists, who want to develop applications using TensorFlow and Keras. This rapid hands-on course quickly shows you how to get to grips with TensorFlow in the context of real-world application development. We assume that you are familiar with Python and have a basic knowledge of web application development. If you have a background in linear algebra, probability, and statistics, you will easily grasp concepts that are discussed in the book.
Luis Capelo is a Harvard-trained analyst and a programmer, who specializes in designing and developing data science products. He is based in New York City, America. Luis is the head of the Data Products team at Forbes, where they investigate new techniques for optimizing article performance and create clever bots that help them distribute their content. Luis worked for the United Nations as part of the Humanitarian Data Exchange team (founders of the Center for Humanitarian Data). Later on, he led a team of scientists at the Flowminder Foundation, developing models for assisting the humanitarian community. Luis is a native of Havana, Cuba, and the founder and owner of a small consultancy firm dedicated to supporting the nascent Cuban private sector.Sie lesen das E-Book in den Legimi-Apps auf:
Seitenzahl: 145
Veröffentlichungsjahr: 2018
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Table of Contents
Beginning Application Development with TensorFlow and Keras
Beginning Application Development with TensorFlow and Keras
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 author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
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Production Coordinator: Vishal Pawar, Samita Warang
First published: April 2018
Production reference: 1300418
Published by Packt Publishing Ltd.
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Contributors
About the author
Luis Capelo is a Harvard-trained analyst and programmer who specializes in the design and development of data science products. He is based in the great New York City, USA.
He is the head of the Data Products team at Forbes, where they both investigate new techniques for optimizing article performance and create clever bots that help them distribute their content. Previously, he led a team of world-class scientists at the Flowminder Foundation, where we developed predictive models for assisting the humanitarian community. Prior to that, he worked for the United Nations as part of the Humanitarian Data Exchange team (founders of the Center for Humanitarian Data).
He is a native of Havana, Cuba, and the founder and owner of a small consultancy firm dedicated to supporting the nascent Cuban private sector.
About the reviewer
Manoj Pandey is a Python programmer and the founder and organizer of PyData Delhi. He works on research and development from time to time, and is currently working with RaRe Technologies on their incubator program for a computational linear algebra project. Prior to this, he has worked with Indian startups and small design/development agencies, and teaches Python/JavaScript to many on Codementor (@manojpandey). You can reach out to him at Twitter: onlyrealmvp.
Preface
TensorFlow is one of the most popular architectures used for machine learning and, more recently, deep learning. This book is your guide to deploy TensorFlow and Keras models into real-world applications.
The book begins with a dedicated blueprint for how to build an application that generates predictions. Each subsequent lesson tackles a particular type of model, such as neural networks, configuring a deep learning environment, using Keras and focuses on the three important questions of how the model works, how to improve our prediction accuracy in our example model, and how to measure and assess its performance using real-world applications.
In this book, you will learn how to create an application that generates predictions from deep learning. This learning journey begins by exploring the common components of a neural network and its essential performance. By end of the lesson you will be exploring a trained neural network created using TensorFlow. In the remaining lessons, you will learn to build a deep learning model with different components together and measuring their performance in prediction. Finally, we will be able to deploy a working web-application
By the end of this book, you will be equipped to create more accurate prediction by creating a completely new model, changing the core components of the application as you see fit.
What This Book Covers
Lesson 1, Introduction to Neural Networks and Deep Learning, helps you set up and configure deep learning environment and start looking at individual models and case studies. It also discusses neural networks and its idea along with their origins and explores their power.
Lesson 2, Model Architecture, shows how to predict Bitcoin prices using deep learning model.
Lesson 3, Model Evaluation and Optimization, shows on how to evaluate a neural network model. We will modify the network's hyperparameters to improve its performance.
Lesson 4, Productization explains how to productize a deep learning model and also provides an exercise of how to deploy a model as a web application.
What You Need for This Book
This book will require the following minimum hardware requirements:
Throughout this book, we will be using Python 3, TensorFlow, TensorBoard, and Keras. Please ensure you have the following installed on your machine:
Who This Book is for
This book is designed for developers, analysts, and data scientists interested in developing applications using TensorFlow and Keras. You need to have programming knowledge. We also assume your familiarity with Python 3 and basic knowledge of web-applications. You also need to have a prior understanding and working knowledge of linear algebra, probability, and statistics.
Conventions
In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.
Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "The \ class provides static methods to generate an instance of itself, such as ()."
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Any command-line input or output is written as follows:
New terms and important words are shown in bold. Words that you see on the screen, for example, in menus or dialog boxes, appear in the text like this: "Clicking the Next button moves you to the next screen."
Note
Warnings or important notes appear in a box like this.
Tip
Tips and tricks appear like this.
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To send us general feedback, simply e-mail <[email protected]>, and mention the book's title in the subject of your message.
If there is a topic that you have expertise in and you are interested in either writing or contributing to a book, see our author guide at www.packtpub.com/authors.
Customer Support
Now that you are the proud owner of a Packt book, we have a number of things to help you to get the most from your purchase.
Downloading the Example Code
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Once the file is downloaded, please make sure that you unzip or extract the folder using the latest version of:
The code bundle for the book is also hosted on GitHub at https://github.com/TrainingByPackt/Beginning-Application-Developmentwith-TensorFlow-and-Keras. We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!
Installation
Before you start with this course, we'll install Visual Studio Code, Python 3, TensorFlow, and Keras. The steps for installation are as follows:
Installing Visual Studio
Installing Python 3
Installing TensorFlow
Download and install TensorFlow by following the instructions on this website:https://www.tensorflow.org/install/install_windows.
Installing Keras
Download and install Keras by following the instructions on this website: https://keras.io/#installation.
Errata
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We appreciate your help in protecting our authors and our ability to bring you valuable content.
Questions
If you have a problem with any aspect of this book, you can contact us at <[email protected]>, and we will do our best to address the problem.
Chapter 1. Introduction to Neural Networks and Deep Learning
In this lesson, we will cover the basics of neural networks and how to set up a deep learning programming environment. We will also explore the common components of a neural network and its essential operations. We will conclude this lesson by exploring a trained neural network created using TensorFlow.
This lesson is about understanding what neural networks can do. We will not cover mathematical concepts underlying deep learning algorithms, but will instead describe the essential pieces that make a deep learning system. We will also look at examples where neural networks have been used to solve real-world problems.
This lesson will give you a practical intuition on how to engineer systems that use neural networks to solve problems—including how to determine if a given problem can be solved at all with such algorithms. At its core, this lesson challenges you to think about your problem as a mathematical representation of ideas. By the end of this lesson, you will be able to think about a problem as a collection of these representations and then start to recognize how these representations may be learned by deep learning algorithms.
Lesson Objectives
By the end of this lesson, you will be able to:
What are Neural Networks?
Neural networks—also known as Artificial Neural Networks—were first proposed in the 40s by MIT professors Warren McCullough and Walter Pitts.
Note
For more information refer, Explained: Neural networks. MIT News Office, April 14, 2017. Available at: http://news.mit.edu/2017/explained-neural-networks-deep-learning-0414.
Inspired by advancements in neuroscience, they proposed to create a computer system that reproduced how the brain works (human or otherwise). At its core was the idea of a computer system that worked as an interconnected network. That is, a system that has many simple components. These components both interpret data and influence each other on how to interpret data. This same core idea remains today.
Deep learning is largely considered the contemporary study of neural networks. Think of it as a current name given to neural networks. The main difference is that the neural networks used in deep learning are typically far greater in size—that is, they have many more nodes and layers—than earlier neural networks. Deep learning algorithms and applications typically require resources to achieve success, hence the use of the word deep to emphasize its size and the large number of interconnected components.
Successful Applications
Neural networks have been under research since their inception in the 40s in one form or another. It is only recently, however, that deep learning systems have been successfully used in large-scale industry applications.
Contemporary
