"I 039 m個のアウト"の翻訳 英語に:
例 (レビューされていない外部ソース)
| m個の手本データセットが | So what is the parameter estimation problem? |
| H I J K L M N | A, B, C, D, E, F, G and I just carry on with the next set of letters in the alphabet, same scale |
| P R I A M O S. | Uh, Priamos. |
| Æ Æ ª I ³ ê A M Š ð | We'll no longer be held in the dark. |
| M個のデータ点があります | Suppose our data looks like this |
| アウト アウト | Next |
| At 3 30 p. m., I stopped by Williams Medical Supply. | I had some books, but... What? Were you nervous? |
| 彼の名前はリチャード キンブル K I M B L Eよ | They'll know. |
| つまり iが1からmまでのfor文 | Next, we're going to loop through our training set. |
| At 8 30 a. m., I set the timer for 15 minutes... | Okay, I'll be patient. |
| Simmer down, I manage your mayhem, I'm bright as the A. M. | Simmer down, I manage your mayhem, I'm bright as the A.M. |
| 1 m 和を取ることの トレーニングセット全体で このx(i) 引くことの y(i) | It turns out this first term simplifies to 1 M, sum over my training set of just that, X(i) Y(i). |
| ノードがn個エッジがm個ある場合ヒープを用いると | Let's look right now at the analysis of the algorithm. |
| アウト | Where's he gone? cried the man with the beard. |
| アウト | You're out! |
| アウト | Out. |
| アウト | OUT! |
| アウト | OUUUUT! |
| アウト | Out! |
| i 1 から m までの差の二乗の総和を行い | So what I want really is to sum over my training set. |
| ３年前にM I T の綴りを発見しました | I brought you guys together again. |
| Mの位置が0 iが1 そしてfが2です | Remember that Python strings and, in fact, almost all Python collections start counting at zero. |
| アウトだ | You are out. |
| アウトだ | That was out. |
| アウトだ | It was in. Out. |
| 三振 アウト! | Number 21, the shortstop, Birdie King. Strike three. |
| ２アウトだ | Two out. Two! Two! |
| テイク アウトに | Would you like a doggy bag? |
| X₁からXMまでのM個のデータ点があるとすると | The optimal or most likely mean is just the average of the data points. |
| 書き下すと 1からmまでに渡り x(i)から | Well the variance, I'll just write out the standard formula again, |
| アウトだこの野郎 | You're fucking out! |
| アウトだこの野郎 | You're fucking out! You're fucking out! |
| その前にはi個のトークンを読んたあとなので 次は計i 1個です このアプローチをシフトと呼びます | The (c) was one token previously we'd seen (i) tokens , so now we've seen i 1 tokens. |
| 三振 バッター アウト | Thanks. Strike three, you're out! |
| 人間とアウト | Out with the humans! |
| アウトだった | That was out. |
| 僕はアウトだ | Yeah, I got nothing. I'm out. |
| m個の手本があるとしよう これらの手本の個々は R nに属するフィーチャーと | Let's say that we have an unlabeled training set of M examples, and each of these examples is going to be a feature in Rn so your training set could be, feature vectors from the last |
| xiとyiのペアがm個あるようなのが あったとする | And suppose we have a training set like this of this of |
| 結局 m掛けるmの | Then, it turns out that the matrix A times |
| 本日２回目のアウト | Your second out of the day! |
| すると M ナイト シャマランばりの神秘的な形で 私は皆さんご存知の文字である M I T という言葉を発見しました シンプリシティーとコンプレクシティーには M I T という文字が含まれています | So, I was in the Cape one time, and I typed the word simplicity, and I discovered, in this weird, M. Night Shyamalan way, that I discovered the letters, M, I, T. You know the word? |
| 1 mの和を トレーニング手本に渡って取ることの y(i)掛けるcost1の | So, what we have for the support vector machine is an minimizationminimalization problem of one of 1 over m, sum over my training examples of y(i) times cost 1 of theta transpose x(i) plus 1 minus y(i) times cost zero of theta transpose x(i). |
| つまりトレーニングセットから m個の手本から それらの平均をとる | So Mu is the mean parameter, so I'm going to take my training set, take my m examples and average them. |
| アトランタはアウトだこの野郎 | Atlanta, you're fucking out. |
