苹果iOS入门：Ray Wenderlich出品的机器学习教程

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"《Machine Learning by Tutorials v0.2》是由Matthijs Hollemans、Chris La Pollo和Audrey Tam合作编写的教程，由Razeware LLC出版，专为Apple和iOS开发者设计，旨在入门机器学习。该教程版权受保护，未经版权所有者事先书面许可，不得复制或分发任何部分，包括文本、图像和源代码。本书和相关材料提供"按原样"使用，不附带任何明示或暗示的保证，包括但不限于适销性、特定用途的适用性和非侵权性。书中强调，作者和版权持有者不对软件本身或因使用软件而产生的任何索赔、损害或其他责任负责，无论是合同、侵权还是其他法律原因。此外，书中提到的所有商标和注册商标均为各自所有者的财产。《Machine Learning by Tutorials》是一本深入浅出的指南，旨在帮助读者掌握在iOS平台上进行机器学习的基础知识和技术，适合那些希望在这个领域起步或扩展技能的开发人员。该教程可能包含理论概念、实践示例以及用于构建实际项目的代码，旨在通过交互式学习和实操来教授读者如何构建机器学习模型，如分类、回归、聚类等，以及如何应用深度学习技术如神经网络在iOS应用中。书中可能还会涵盖数据预处理、特征工程、模型训练和评估的关键步骤，同时介绍常用的机器学习库和框架，如TensorFlow Lite和Core ML。为了方便读者，教程可能以网站的形式（raywenderlich.com）提供配套资源，包括视频教程、练习项目和社区支持，以确保学习者能够全面理解和应用所学知识。最后，这本书是对Floortje的献词，感谢她带来的陪伴和支持。《Machine Learning by Tutorials v0.2》是为iOS开发者打造的一本实用且系统化的机器学习入门教材，适合希望通过实战学习并在移动应用中实现智能功能的专业人士使用。"

Chapter 1: Machine

Learning, iOS & You

By Audrey Tam & Matthijs Hollemans

Want to know a secret? Machine learning isn't really that hard to learn. The truth is,

you don't need a PhD from a prestigious university or a background in mathematics to

do machine learning. Sure, there are a few new concepts to wrap your head around,

there is a lot of jargon, and it takes a while to feel at home in the ever-growing

ecosystem of tools and libraries. But if you already know how to code, you can pick up

machine learning quite easily — promise!

This book helps you get started with machine learning on iOS and Apple devices. This

ﬁrst chapter is a gentle introduction to the world of machine learning and what it has to

offer — as well as what its limitations are. In the rest of the book, you'll look at each of

these topics in more detail, until you know just enough to be dangerous! Naturally, a

book like this can't cover the entire ﬁeld of machine learning, but you'll learn enough to

make machine learning a useful tool in your software development toolbox.

With every new version of iOS, Apple is making it easier to add machine learning to

your apps. There are now several high-level frameworks, including Natural Language,

Speech, and Vision, that provide advanced machine learning functionality behind

simple APIs as part of Apple's iOS tooling. Want to convert speech to text or text to

speech? Want to recognize language or grammatical structure? Want to detect faces in

photos or track moving objects in video? These built-in frameworks have got you

covered.

For more control, the Core ML and Metal Performance Shaders frameworks let you run

your own machine-learning models. With these APIs, you can now add state-of-the-art

machine-learning technology to your apps! Apple also provides easy-to-use tools such

as Create ML and Turi Create that let you build your own models for use with Core ML.

And many of the industry-standard machine-learning tools can export Core ML models,

too, allowing you to integrate them into your iOS apps with ease.

raywenderlich.com

In this book, you'll learn how to use these tools and frameworks to make your apps

smarter. Even better, you'll learn how machine learning works behind the scenes — and

why this technology is awesome.

What is machine learning?

Machine learning is hot and exciting — but it's not exactly new. Many companies have

been routinely employing machine learning as part of their daily business for several

decades already. Google, perhaps the quintessential machine-learning company, was

founded in 1998 when Larry Page invented PageRank, now considered to be a classic

machine-learning algorithm.

But machine learning goes even further back, all the way to the early days of modern

computers. In 1959, Arthur Samuel deﬁned machine learning as the ﬁeld of study that

gives computers the ability to learn without being explicitly programmed.

In fact, the most basic machine-learning algorithm of them all, linear regression or the

"method of least squares," was invented over 200 years ago by famed mathematician

Carl Friedrich Gauss. That's approximately one-and-a-half centuries before there were

computers... even before electricity was common. This simple algorithm is still used

today and is the foundation of more complex methods such as logistic regression and

even neural networks — all algorithms that you'll learn about in this book.

Even deep learning, which had its big breakthrough moment in 2012 when a so-called

convolutional neural network overwhelmingly won the ImageNet Large Scale Visual

Recognition Challenge, is based on the ideas of artiﬁcial neural networks dating back to

the work of McCulloch and Pitts in the early 1940s when people started to wonder if it

would be possible to make computers that worked like the human brain.

So, yes, machine learning has been around for a while. But that doesn't mean you've

missed the boat. On the contrary, the reason it's become such a hot topic recently is

that machine learning works best when there is a lot of data — thanks to the internet

and smartphones, there is now more data than ever. Moreover, computing power has

become much cheaper. It took a while for it to catch on, but machine learning has

grown into a practical tool for solving real-world problems that were too complex to

deal with before.

What is new, and why we've written this book, is that mobile devices are now powerful

enough to run machine-learning algorithms right in the palm of your hand!

Machine Learning by Tutorials Chapter 1: Machine Learning, iOS & You

raywenderlich.com

Learning without explicit programming

So what exactly do we mean when we say, "machine learning"?

As a programmer, you're used to writing code that tells the computer exactly what to do

in any given situation. A lot of this code consists of rules:

if this is true,

then do something,

else do another thing

This is pretty much how software has always been written. Different programmers use

different languages, but they're all essentially writing long lists of instructions for the

computer to perform. And this works very well for a lot of software, which is why it's

such a popular approach.

Writing out if-then-else rules works well for automating repetitive tasks that most

people ﬁnd boring or that require long periods of concentration. It's possible, though

time-consuming, to feed a computer a lot of knowledge in the form of such rules, then

program it to mimic what people do consciously, meaning to!reason logically with the

rules or knowledge, or to apply heuristics, meaning strategies or rules of thumb.

But there are also many interesting problems in which it's hard to come up with a

suitable set of rules or in which heuristics are too crude — and this is where machine

learning can help. It's very hard to explicitly program computers to do the kinds of

things most people do without conscious thought: recognizing faces, expressions and

emotions, the shape of objects, or the sense or style of sentences. It's hard to write

down the algorithms for these tasks: What is it that the human brain actually does to

accomplish these tasks?

How would you write down rules to recognize a face in a photo? Using the RGB values of

the pixels for hair, skin or eye color aren't reliable. Hair style, glasses, makeup, beards

and mustaches can change your appearance between photos. Most photos won't be of a

person looking straight at the camera, so you'd have to account for many different

camera angles. You'd end up with hundreds, if not thousands, of rules, and they still

wouldn't cover all possible situations.

How do your friends recognize you as you and not a sibling or relative who resembles

you? How do you explain how to distinguish cats from dogs to a small child, if you only

have photos? What rules differentiate between cat and dog faces? Dogs and cats come

in many different colors and hair lengths and tail shapes. For every rule you think of,

there will probably be a lot of exceptions.

Machine Learning by Tutorials Chapter 1: Machine Learning, iOS & You

raywenderlich.com

The big idea behind machine learning is that, if you can't write the exact steps for a

computer to recognize objects in an image or the sentiment expressed by some text,

maybe you can write a program to produce an algorithm that does the job.

Instead of having a domain expert design and implement if-then-else rules, you can

let the computer learn the rules to solve these kinds of problems from examples. And

that's exactly what machine learning is: using a learning algorithm that can

automatically derive the "rules" that are needed to solve a certain problem. Often, such

an automated learner comes up with better rules than humans can, because it can ﬁnd

patterns in the data that humans don't see.

Deep learning

Until now, we've been using terms like deep learning and neural networks pretty liberally.

Let's take a moment to properly deﬁne what these terms mean.

Neural networks are made up of layers of nodes (neurons) in an attempt to mimic how

the human brain works. For a long time, this was mostly theoretical: Only simple neural

networks with a couple of layers could be computed in a reasonable time with the

computers of that era. In addition, there were problems with the math, and networks

with more and larger layers just didn't work very well.

It took until the mid 2000s for computer scientists to ﬁgure out how to train really deep

networks consisting of many layers. At the same time, the market for computer game

devices exploded, spurring demand for faster, cheaper GPUs to run ever more elaborate

games. GPUs (Graphics Processing Units), speed up graphics and are great at doing lots

of matrix operations very fast. As it happens, neural networks also require lots of matrix

operations. Thanks to gamers, fast GPUs became very affordable, and that's exactly the

sort of hardware needed to train deep multi-layer neural networks. A lot of the most

exciting progress in machine learning is driven by deep learning, which uses neural

networks with a high number of layers, and a high number of neurons at each layer as

its learning algorithm.

Note: Companies like Apple, Intel and Google are designing processing units

speciﬁcally designed for deep learning, such as Google's TPU, or Tensor Processing

Unit, and the new Neural Engine in the iPhone XS's A12 processor, which lack of

the 3D rendering capabilities of GPUs and instead can run the computational

needs of the neural networks much more efﬁciently.

Machine Learning by Tutorials Chapter 1: Machine Learning, iOS & You

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