使用F#构建.NET智能应用：机器学习实战

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"Machine Learning Projects for .NET Developers 是一本针对.NET开发者的学习机器学习技术的实践指南，通过一系列项目教你如何构建能从数据中学习的智能应用程序。书中使用F#语言来实现机器学习逻辑，F#在.NET环境中非常适合用于机器学习应用。这本书适合有一定C#基础的中级到高级.NET开发者，无论你是否熟悉F#，都可以从中受益。在书中，你将通过以下项目学习： 1. 从零开始构建光学字符识别（OCR）系统。 2. 编写一个基于样例学习的垃圾邮件过滤器。 3. 使用F#的强力类型提供者与外部资源（如R编程语言的数据分析工具）交互。 4. 对数据进行特征转换，并利用这些特征做出准确预测。 5. 在未知模式的情况下在数据中寻找模式。 6. 利用回归模型预测数值值。 7. 实现一个通过经验学习玩游戏的智能游戏。 8. 优化和扩展你的算法代码。你将学到的关键知识包括： - 机器学习的基础概念和领域概述。 - 如何识别问题模式并解决它们。 - 简单预测算法的理解与应用。 - 模型的开发、诊断和调优。 - 使用F#编写优雅、高效且无bug的函数式代码。本书的目标读者是中高级.NET开发者，具备C#基础，无需事先了解机器学习技术。即使对F#不熟悉，书中也会引导入门。如果你热爱编程和数据分析，这本书非常适合你。书中的章节涵盖了： - 第1章：256种灰度 - 自动识别数字图像的程序 - 第2章：垃圾还是正常？使用贝叶斯定理检测文本中的垃圾邮件 - 第3章：类型提供者的乐趣 - 从任何地方找到和准备数据 - 第4章：自行车与人 - 使用梯度下降法拟合回归模型到数据 - 第5章：你并不独特 - 使用聚类和主成分分析检测模式 - 第6章：树与森林 - 在不完整数据上进行预测 - 第7章：奇异的游戏 - 使用强化学习从经验中学习 - 第8章：数字重访 - 优化和扩展你的算法代码 - 第9章：结论此外，这本书的重点在于简单但有效的机器学习方法，而不是深入复杂的数学理论。它强调如何将这些方法实际应用到各种真实世界的场景和行业中，如广告、金融、医疗和科学研究。" 通过这本书，你将掌握如何在.NET环境中运用机器学习技术，提升你的应用程序的智能水平，同时深化对F#语言的理解和使用。无论是新接触机器学习还是寻求扩展F#应用的开发者，都能在本书中找到有价值的指导和启发。

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

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?

What Is Machine Learning?

But first, what is machine learning? At its core, machine learning is writing programs

that learn how to perform a task from experience, without being explicitly programmed

to do so. This is still a fuzzy definition, and begs the question: How do you define

learning, exactly? A somewhat dry definition is the following: A program is learning if,

as it is given more data points, it becomes automatically better at performing a given

task. Another way to look at it is by flipping around the definition: If you keep doing the

same thing over and over again, regardless of the results you observe, you are certainly

not learning.

This definition summarizes fairly well what “doing machine learning” is about.

Your goal is to write a program that will perform some task automatically. The program

should be able to learn from experience, either in the form of a pre-existing dataset of

past observations, or in the form of data accumulated by the program itself as it

performs its job (what’s known as “online learning”). As more data becomes available,

the program should become better at the task without your having to modify the code of

the program itself.

Your job in writing such a program involves a couple of ingredients. First, your

program will need data it can learn from. A significant part of machine learning

revolves around gathering and preparing data to be in a form your program will be able

to use. This process of reorganizing raw data into a format that better represents the

problem domain and that can be understood by your program is called feature

extraction.

Then, your program needs to be able to understand how well it is performing its

task, so that it can adjust and learn from experience. Thus, it is crucial to define a

measure that properly captures what it means to “do the task” well or badly.

Finally, machine learning requires some patience, an inquisitive mind, and a lot of

creativity! You will need to pick an algorithm, feed it data to train a predictive model,

validate how well the model performs, and potentially refine and iterate, maybe by

defining new features, or maybe by picking a new algorithm. This cycle—learning from

training data, evaluating from validation data, and refining—is at the heart of the

machine learning process. This is the scientific method in action: You are trying to

identify a model that adequately predicts the world by formulating hypotheses and

conducting a series of validation experiments to decide how to move forward.

Before we dive into our first problem, two quick comments. First, this might sound

like a broad description, and it is. Machine learning applies to a large spectrum of

problems, ranging all the way from detecting spam email and self-driving cars to

recommending movies you might enjoy, automatic translation, or using medical data to

help with diagnostics. While each domain has its specificities and needs to be well

understood in order to successfully apply machine learning techniques, the principles

and methods remain largely the same.

Then, note how our machine learning definition explicitly mentions “writing

programs.” Unlike with statistics, which is mostly concerned with validating whether or

not a model is correct, the end goal of machine learning is to create a program that runs

in production. As such, it makes it a very interesting area to work in, first because it is

by nature cross-disciplinary (it is difficult to be an expert in both statistical methods and

software engineering), and then because it opens up a very exciting new field for

software engineers.

Now that we have a basic definition in place, let’s dive into our first problem.

A Classic Machine Learning Problem:

Classifying Images

Recognizing images, and human handwriting in particular, is a classic problem in

machine learning. First, it is a problem with extremely useful applications.

Automatically recognizing addresses or zip codes on letters allows the post office to

efficiently dispatch letters, sparing someone the tedious task of sorting them manually;

being able to deposit a check in an ATM machine, which recognizes amounts, speeds up

the process of getting the funds into your account, and reduces the need to wait in line at

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使用F#构建.NET智能应用：机器学习实战

MachineLearningProjectsfor.NETDevelopers.pdf 英文原版

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

Machine Learning Projects for .NET Developers 源代码

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

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