MachineLearningProjectsfor.NETDevelopers(Apress,2015)

4星 · 超过85%的资源需积分: 9 187 浏览量更新于2023-03-16 评论收藏 7.23MB PDF 举报

身份认证购VIP最低享 7 折!

领优惠券(最高得80元）

资源详情

资源评论

资源推荐

Brandewinder

Shelve in

.NET

User level:

Intermediate–Advanced

www.apress.com

SOURCE CODE ONLINE

BOOKS FOR PROFESSIONALS BY PROFESSIONALS

Machine Learning Projects for

.NET Developers

Machine Learning Projects for .NET Developers shows you how to build smarter

.NET applications that learn from data, using simple algorithms and techniques

that can be applied to a wide range of real-world problems. You’ll code each

project in the familiar setting of Visual Studio, while the machine learning logic

uses F#, a language ideally suited to machine learning applications in .NET.

If you’re new to F#, this book will give you everything you need to get started.

If you’re already familiar with F#, this is your chance to put the language into

action in an exciting new context.

In a series of fascinating projects, you’ll learn how to:

• Build an optical character recognition (OCR) system from scratch

• Code a spam filter that learns by example

• Use F#’s powerful type providers to interface with external resources

(in this case, data analysis tools from the R programming language)

• Transform your data into informative features, and use them to make

accurate predictions

• Find patterns in data when you don’t know what you’re looking for

• Predict numerical values using regression models

• Implement an intelligent game that learns how to play from experience

Along the way, you’ll learn fundamental ideas that can be applied in all kinds of

real-world contexts and industries, from advertising to finance, medicine, and

scientific research. While some machine learning algorithms use fairly advanced

mathematics, this book focuses on simple but effective approaches. If you enjoy

hacking code and data, this book is for you.

9781430 267676

54999

ISBN 978-1-4302-6767-6

www.it-ebooks.info

iii

Contents at a Glance

About the Author �� xiii

About the Technical Reviewer ��xv

Acknowledgments ��xvii

Introduction ��xix

■Chapter 1: 256 Shades of Gray �� 1

■Chapter 2: Spam or Ham? �� 33

■Chapter 3: The Joy of Type Providers �� 67

■Chapter 4: Of Bikes and Men �� 93

■Chapter 5: You Are Not a Unique Snowﬂake �� 131

■Chapter 6: Trees and Forests �� 179

■Chapter 7: A Strange Game �� 211

■Chapter 8: Digits, Revisited �� 239

■Chapter 9: Conclusion �� 267

Index �� 271

www.it-ebooks.info

xix

Introduction

If you are holding this book, I have to assume that you are a .NET developer interested in machine learning.

You are probably comfortable with writing applications in C#, most likely line-of-business applications.

Maybe you have encountered F# before, maybe not. And you are very probably curious about machine

learning. The topic is getting more press every day, as it has a strong connection to software engineering, but

it also uses unfamiliar methods and seemingly abstract mathematical concepts. In short, machine learning

looks like an interesting topic, and a useful skill to learn, but it’s difficult to figure out where to start.

This book is intended as an introduction to machine learning for developers. My main goal in writing

it was to make the topic accessible to a reader who is comfortable writing code, and is not a mathematician.

A taste for mathematics certainly doesn’t hurt, but this book is about learning some of the core concepts

through code by using practical examples that illustrate how and why things work.

But first, what is machine learning? Machine learning is the art of writing computer programs that get

better at performing a task as more data becomes available, without requiring you, the developer, to change

the code.

This is a fairly broad definition, which reflects the fact that machine learning applies to a very broad

range of domains. However, some specific aspects of that definition are worth pointing out more closely.

Machine learning is about writing programs—code that runs in production and performs a task—which

makes it different from statistics, for instance. Machine learning is a cross-disciplinary area, and is a topic

relevant to both the mathematically-inclined researcher and the software engineer.

The other interesting piece in that definition is data. Machine learning is about solving practical

problems using the data you have available. Working with data is a key part of machine learning;

understanding your data and learning how to extract useful information from it are quite often more

important than the specific algorithm you will use. For that reason, we will approach machine learning

starting with data. Each chapter will begin with a real dataset, with all its real-world imperfections and

surprises, and a specific problem we want to address. And, starting from there, we will build a solution to the

problem from the ground up, introducing ideas as we need them, in context. As we do so, we will create a

foundation that will help you understand how different ideas work together, and will make it easy later on to

productively use libraries or frameworks, if you need them.

Our exploration will start in the familiar grounds of C# and Visual Studio, but as we progress we will

introduce F#, a .NET language that is particularly suited for machine learning problems. Just like machine

learning, programming in a functional style can be intimidating at first. However, once you get the hang of it,

F# is both simple and extremely productive. If you are a complete F# beginner, this book will walk you through

what you need to know about the language, and you will learn how to use it productively on real-world,

interesting problems.

Along the way, we will explore a whole range of diverse problems, which will give you a sense for the

many places and perhaps unexpected ways that machine learning can make your applications better. We

will explore image recognition, spam filters, and a self-learning game, and much more. And, as we take that

journey together, you will see that machine learning is not all that complicated, and that fairly simple models

can produce surprisingly good results. And, last but not least, you will see that machine learning is a lot of

fun! So, without further ado, let’s start hacking on our first machine learning problem.

www.it-ebooks.info

Chapter 1

256 Shades of Gray

Building a Program to Automatically Recognize

Images of Numbers

If you were to create a list of current hot topics in technology, machine learning would certainly be

somewhere among the top spots. And yet, while the term shows up everywhere, what it means exactly is

often shrouded in confusion. Is it the same thing as “big data,” or perhaps “data science”? How is it different

from statistics? On the surface, machine learning might appear to be an exotic and intimidating specialty

that uses fancy mathematics and algorithms, with little in common with the daily activities of a software

engineer.

In this chapter, and in the rest of this book, my goal will be to demystify machine learning by working

through real-world projects together. We will solve problems step by step, primarily writing code from

the ground up. By taking this approach, we will be able to understand the nuts and bolts of how things

work, illustrating along the way core ideas and methods that are broadly applicable, and giving you a solid

foundation on which to build specialized libraries later on. In our first chapter, we will dive right in with a

classic problem—recognizing hand-written digits—doing a couple of things along the way:

• Establish a methodology applicable across most machine learning problems.

Developing a machine learning model is subtly different from writing standard

line-of-business applications, and it comes with specific challenges. At the end of

this chapter, you will understand the notion of cross-validation, why it matters, and

how to use it.

• Get you to understand how to “think machine learning” and how to look at ML

problems. We will discuss ideas like similarity and distance, which are central

to most algorithms. We will also show that while mathematics is an important

ingredient of machine learning, that aspect tends to be over-emphasized, and some

of the core ideas are actually fairly simple. We will start with a rather straightforward

algorithm and see that it actually works pretty well!

• Know how to approach the problem in C# and F#. We’ll begin with implementing the

solution in C# and then present the equivalent solution in F#, a .NET language that is

uniquely suited for machine learning and data science.

Tackling such a problem head on in the first chapter might sound like a daunting task at first—but

don’t be intimidated! It is a hard problem on the surface, but as you will see, we will be able to create a

pretty effective solution using only fairly simple methods. Besides, where would be the fun in solving trivial

toy problems?

www.it-ebooks.info

剩余289页未读，继续阅读

isshh

2017-07-31

这本书用来机器学习入门非常不错。

vanridin

粉丝: 108
资源: 1192

会员权益专享

Machine Learning Projects for .NET Developers(Apress,2015)

评论2

会员权益专享

最新资源

Machine Learning Projects for .NET Developers(Apress,2015)

评论2

MachineLearningProjectsfor.NETDevelopers.pdf 英文原版

Machine Learning for Developers 无水印原版pdf

Machine.Learning.Projects.for.NET.Developers.1430267674

.net maui for c# developers .net maui for c# developers

unity ml.net

python orion-ml

Eclipse IDE for Java Developers、Eclipse IDE for Java EE Developers区别

eclipse ide for java developers

unity barracuda

PaddleOCRSharp

.net5.0 log4

Eclipse IDE for Java Developers免费吗

eclipse ide for java developers - 2022-09

Eclipse IDE for Java Developers能开发APP吗

TensorFlow Java

edge impulse

Kotlin+.netCore相关的参考文献

AutoCAD .NET API

asp.net core mvc

Pytorch fastai

会员权益专享

最新资源