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  • YUFENG GUO: The world is filled with data, a lot of data--

  • pictures, music, words, spreadsheets, videos, and it

  • doesn't look like it's going to slow down anytime soon.

  • Machine learning brings the promise

  • of deriving meaning from all of that data.

  • Arthur C. Clarke famously once said,

  • "Any sufficiently advanced technology is

  • indistinguishable from magic."

  • I found machine learning not to be magic,

  • but rather tools and technology that you

  • can utilize to answer questions with your data.

  • This is Cloud AI Adventures.

  • My name is Yufeng Guo, and each episode,

  • we will be exploring the art, science,

  • and tools of machine learning.

  • Along the way, we'll see just how easy

  • it is to create amazing experiences

  • and yield valuable insights.

  • The value of machine learning is only

  • just beginning to show itself.

  • There is a lot of data in the world today generated

  • not only by people, but also by computers, phones

  • and other devices.

  • This will only continue to grow in the years to come.

  • Traditionally, humans have analyzed data

  • and adapted systems to the changes in data patterns.

  • However, as the volume of data surpasses

  • the ability for humans to make sense of it

  • and manually write those rules, we

  • will turn increasingly to automated systems that

  • can learn from the data and importantly,

  • the changes in data to adapt to a shifting landscape.

  • We see machine learning all around us

  • in the products we use today.

  • However, it isn't always apparent

  • that machine learning is behind it all.

  • While things like tagging objects and people inside

  • of photos are clearly machine learning at play,

  • it may not be immediately apparent

  • that recommending the next video to watch

  • is also powered by machine learning.

  • Of course, perhaps the biggest example of all

  • is Google search.

  • Every time you use Google search,

  • you're using a system that has many machine learning systems

  • at its core, from understanding the text of your query

  • to adjusting the results based on your personal interests,

  • such as knowing which results to show you first when searching

  • for Java depending on whether you're a coffee expert

  • or a developer-- perhaps you're both.

  • Today, machine learning's immediate applications

  • are already quite wide-ranging, including image recognition,

  • fraud detection and recommendation systems,

  • as well as text and speech systems too.

  • These powerful capabilities can be

  • applied to a wide range of fields,

  • from diabetic retinopathy and skin cancer detection to retail

  • and of course, transportation in the form

  • of self-parking and self-driving vehicles.

  • It wasn't that long ago that when a company or product had

  • machine learning in its offerings,

  • it was considered novel.

  • Now, every company is pivoting to use machine learning

  • in their products in some way.

  • It's rapidly becoming, well, an expected feature.

  • Just as we expect companies to have a website that

  • works on your mobile device or perhaps an app,

  • the day will soon come when it will

  • be expected that our technology will

  • be personalized, insightful and self-correcting.

  • As we use machine learning to make human tasks better, faster

  • and easier than before, we can also

  • look further into the future when machine learning

  • can help us do tasks that we never

  • could have achieved on our own.

  • Thankfully, it's not hard to take advantage

  • of machine learning today.

  • The tooling has gotten quite good.

  • All you need is data, developers and a willingness

  • to take the plunge.

  • For our purposes, I've shortened the definition

  • of machine learning down to just five words--

  • using data to answer questions.

  • While I wouldn't use such a short answer

  • for an essay prompt on exam, it serves a useful purpose for us

  • here.

  • In particular, we can split the definition into two parts--

  • using data and answer questions.

  • These two pieces broadly outline the two sides

  • in machine learning, both of them equally important.

  • Using data is what we refer to as training,

  • while answering questions is referred to as making

  • predictions or inference.

  • Now let's drill into those two sides briefly for a little bit.

  • Training refers to using our data

  • to inform the creation and fine tuning of a predictive model.

  • This predictive model can then be

  • used to serve up predictions on previously unseen data

  • and answer those questions.

  • As more data is gathered, the model

  • can be improved over time and new predictive models deployed.

  • As you may have noticed, the key component

  • of this entire process is data.

  • Everything hinges on data.

  • Data is the key to unlocking machine learning, just

  • as much as machine learning is the key to unlocking

  • that hidden insight in data.

  • This was just a high level overview

  • of machine learning-- why it's useful

  • and some of its applications.

  • Machine learning is a broad field,

  • spanning an entire family of techniques when

  • inferring answers from data.

  • So in future episodes, we'll aim to give you

  • a better sense of what approaches

  • to use for a given data set and question

  • you want to answer, as well as provide the tools for how

  • to accomplish it.

  • In our next episode, we'll dive right

  • into the concrete process of doing machine learning

  • in more detail, going through a step-by-step formula for how

  • to approach machine learning problems.

  • [MUSIC PLAYING]

YUFENG GUO: The world is filled with data, a lot of data--

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什麼是機器學習? (What is Machine Learning?)

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    Steven Wu 發佈於 2021 年 01 月 14 日
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