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英語学習

備忘録:Cousera machinelearningで英語を勉強する。(week1 introduction)

CouseraMachinelearning授業における英語の文構造と単語を解説するだけの記事です。
日本語訳はInteractive TranscriptでJapaneseを選択すると下記文章の日本語訳が表示されます。
また、太線はわからなかった単語と文章です。(下に意味と構造を載せてあります)

Week1 introduction

Welcome to this free online class on machine learning.
Machine learning is one of the most exciting recent technologies.
And in this class, you learn about the state of the art and also gain practice implementing and deploying these algorithms yourself.
You've probably use a learning algorithm dozens of times a day without knowing it.
Every time you use a web search engine like Google or Bing to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages.
Every time you use Facebook or Apple's photo typing application and it recognizes your friends' photos, that's also machine learning.
Every time you read your email and your spam filter saves you from having to wade through tons of spam email, that's also a learning algorithm.
For me one of the reasons I'm excited is the AI dream of someday building machines as intelligent as you or me.
We're a long way away from that goal, but many AI researchers believe that the best way to towards that goal is through learning algorithms that try to mimic how the human brain learns.
I'll tell you a little bit about that too in this class. In this class you learn about state-of-the-art machine learning algorithms.
But it turns out just knowing the algorithms and knowing the math isn't that much good if you don't also know how to actually get this stuff to work on problems that you care about.

単語メモ

state of the art 最先端
deploy 運用する
dozens of times 数十回
Every time S V, SがVするたびに
save O from doing Oが~することを省く
wade 処理する
a long way away from ~から遠く離れた
mimic 模倣する
a little bit 若干

文解説:

But it turns out just knowing the algorithms and knowing the math isn't that much good if you don't also know how to actually get this stuff to work on problems that you care about

get O to不定詞 Oに~させる
このgetは使役動詞と呼ばれるもので、他に似た意味を持つ動詞は have O 動詞の原形
これら2つの使い分けは相手にそうしてもらえるのが当然の時はhaveを使い、相手を説得して何かをしてもらうときはgetをつかう。
またthis stuff はthe algorithms and knowing the math を指す。

続き

So, we've also spent a lot of time developing exercises for you to implement each of these algorithms and see how they work for yourself. So why is machine learning so prevalent today?
It turns out that machine learning is a field that had grown out of the field of AI, or artificial intelligence.
We wanted to build intelligent machines and it turns out that there are a few basic things that we could program a machine to do such as how to find the shortest path from A to B.
But for the most part we just did not know how to write AI programs to do the more interesting things such as web search or photo tagging or email anti-spam.
There was a realization that the only way to do these things was to have a machine learn to do it by itself.
So, machine learning was developed as a new capability for computers and today it touches many segments of industry and basic science. For me, I work on machine learning and in a typical week I might end up talking to helicopter pilots, biologists, a bunch of computer systems people (so my colleagues here at Stanford) and averaging two or three times a week I get email from people in industry from Silicon Valley contacting me who have an interest in applying learning algorithms to their own problems.
This is a sign of the range of problems that machine learning touches.
There is autonomous robotics, computational biology, tons of things in Silicon Valley that machine learning is having an impact on.

単語メモ

spend time doing ~するのに時間を費やす
prevalent 普及している
for the most part 大部分において
realization 実現
touch ~に影響を与える
segment 分野、部分
a bunch 一味、集団
autonomous自律した

文章解説

There was a realization that the only way to do these things was to have a machine learnto do it by itself.

have O 動詞の原形 Oに~させる(使役動詞)
2度目の使役動詞登場です。解説は上記を参照。
機械に自らそうすることを学ばせる(機械が自らそうすることを学ぶ)という意味になります。

For me, I work on machine learning and in a typical week I might end up talking to helicopter pilots, biologists, a bunch of computer systems people (so my colleagues here at Stanford) and averaging two or three times a week I get email from people in industry from Silicon Valley contacting me who have an interest in applying learning algorithms to their own problems.

長い文章ですがandで区切ると

①I work on machine learning
in a typical week I might end up talking to helicopter pilots, biologists, a bunch of computer systems people (so my colleagues here at Stanford)
averaging two or three times a week I get email from people in industry from Silicon Valley contacting me who have an interest in applying learning algorithms to their own problems.

で分けられます。in a typical weekとaveraging two or three times a weekが文頭に来てややこしいので、自分も含めて読解が苦手な方は<>で区切るなりしましょう。