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  • Welcome to the Machine Learning Crash Course.

  • My name is Peter Norvig, and when I joined Google in 2001,

  • my title was "Director of Machine Learning,"

  • because I knew then that Machine Learning would be a valuable tool

  • to help engineers, at Google and everywhere else,

  • make sense of their data.

  • I didn't quite anticipate then how widespread the tools would become,

  • and how much demand there would be for engineers who are skilled at using them.

  • This course is designed to set you along the path

  • to becoming a skilled practitioner of the art.

  • What you learn here will allow you, as a software engineer,

  • to do three things better.

  • First, it gives you a tool to reduce the time you spend programming.

  • Suppose I wanted to write a program to correct spelling errors.

  • I could make my way through lots of examples and rules of thumb,

  • like I before E except after C,

  • and after weeks of hard work come up with a reasonable program.

  • Or, I could use an off-the-shelf machine learning tool, feed it some examples,

  • and get a more reliable program in a small fraction of the time.

  • Second, it will allow you to customize your products,

  • making them better for specific groups of people.

  • Suppose I produced my English spelling corrector by writing code by hand,

  • and it was so successful that I wanted to have versions

  • in the 100 most popular languages.

  • I would have to start almost from scratch for each language,

  • and it would take years of effort.

  • But if I built it using machine learning, then moving to another language,

  • to a first approximation, means just collecting data

  • in that language and feeding it into the exact same machine learning model.

  • And third, machine learning lets you solve problems that you, as a programmer,

  • have no idea how to do by hand.

  • As a human being, I have the ability to recognize my friends'

  • faces and understand their speech, but I do all of this subconsciously

  • so if you asked me to write down a program to do it,

  • I'd be completely baffled.

  • But these are tasks that machine learning algorithms do very well;

  • I don't need to tell the algorithm what to do, I only need to show

  • the algorithm lots of examples, and from that the task can be solved.

  • Now, besides these three practical reasons for mastering machine learning,

  • there's a philosophical reason:

  • Machine learning changes the way you think about a problem.

  • Software engineers are trained to think logically and mathematically;

  • we use assertions to prove properties of our program are correct.

  • With machine learning, the focus shifts from a mathematical science

  • to a natural science: we're making observations about an uncertain world,

  • running experiments, and using statistics, not logic,

  • to analyze the results of the experiment.

  • The ability to think like a scientist will expand your horizons

  • and open up new areas that you couldn't explore without it.

  • So enjoy the journey, and happy exploring.

Welcome to the Machine Learning Crash Course.


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B1 中級 美國腔

機器學習介紹 (Introduction to Machine Learning)

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    Lai Kimu 發佈於 2021 年 01 月 14 日