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気楽に等身大☆初めての機械学習
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気楽に等身大☆初めての機械学習
1.
気楽に 等身大☆初めての機械学習 2015年 クラウド温泉@小樽
2.
自己紹介 ▪ 株式会社アイアンドディー ▪ 福重
美香 ▪ Twitter: mi_kodayo ▪ 仕事: RoR、AWS、php、必要なもの ▪ 趣味: 工作
3.
今回、クラウド温泉テーマ MLらしいよ
4.
これを機に、 触ったことないけど 機械学習でLTする!
5.
私の機械学習レベル:初心者 ▪ 大学文系、統計、分析、数学に関する知識なし。三角関 数は女子でも必要だと思います。 ▪ 東京Rに出てみる
=>ゲーム会社系の人が多いなあ・・ ▪ Kaggleなる懸賞サイトがあるのを知り興味は増す ▪ パターン認識の勉強会に出てみる=>数式で死亡 ▪ Hadoop、Apache Mahout、H2O、OSCでおなじみの キーワードだけは押さえつつ ▪ ふらふらしつつ、なかなか一歩を踏み出せずにいた
6.
機械学習とは? (私のイメージ) ▪ 大量の既存データを使って ▪ ある法則でそれを分析し、場合によっては蓄積して いく ▪
経験をためて、その経験を生かして色々できるよう なっていく人間の人生みたいなイメージ。 ▪ ためる経験と、どう生かしたいか?で法則と分析を 変えていく。
7.
機械学習で何ができるの? (私のイメージ) ▪ 分析結果を活用し、新しいデータが来たときの対応判 断ができる。 ▪ アイディア次第でなんでもできるのでは ▪
とりあえずデータ突っ込んで、片っ端から手法にかけ ていけば何か新しい発見があるのかも? ▪ その結果によって、したいことが決まるかも。
8.
機械学習、具体例 ▪ ネコの画像を8000枚くらい収集。ネコの大まかな特 徴を大量データから分析して保持。 ▪ ↓ ▪
新しいネコ画像をwebからダウンロードして、上記分 析ロジックにかける。 ▪ ネコと判断されれば、いい感じに学習できている。ネ コ以外と判断されればデータ見直し
9.
そんな感じで、 機械学習やりますか!!
10.
何がやりたいか決まっていないと、 法則と分析ツールの選択肢が ありすぎて迷う ▪ Scikit-learn ▪ R ▪
Caffe ▪ H2O ▪ Chainer ▪ Deeplearning4j ▪ ほかもたくさん!!それぞれできるML、DLに差分があるのは分ベストプラ クティスが分からない!
11.
私「機械学習やりたいんです けど、言語とか環境問わない です、オススメあります?」 ※現在メインで使っているRubyはMLには向かないということで、もうなんでもこいの状態
12.
とある頭のいい大学生 「scikit-learnおすすめです よ」
13.
scikit-learnに決定 ▪ Pythonのライブラリ ▪ http://scikit-learn.org/0.15/index.html ▪
いろんな分析ができる↓ ▪ チートシート ▪ http://scikit- learn.org/stable/tutorial/machine_learning_map/ ▪ Exampleコードも充実している、
14.
環境構築自分でガチると、 めんどいです ▪ AWSのマイクロ作って、numpy, scipyなど必要なライブラリ をコンパイルして入れていった。 ▪
AWS Microだと、デフォswapが0。scipymake install中にメ モリ不足で失敗するので、以下作業必須 ▪ sudo /bin/dd if=/dev/zero of=/var/swap.1 bs=1M count=1024 ▪ sudo /sbin/mkswap /var/swap.1 ▪ sudo /sbin/swapon /var/swap.1
15.
半日かかる。make祭手いたい・・ えっ!環境を作ってる?漢ですね でもSagemath Cloud を使うと楽ですよ?! もしくはanacondaを使いましょ~。
16.
まずはじめに、 tweetのクラスタ化を写経します http://parosky.net/note/2013111 2
17.
Mecab 形態素解析 #!/bin/env python #coding:utf-8 import
MeCab tagger = MeCab.Tagger("-Ochasen") print tagger.parse("今日はクラウド温泉の日ですー。 いえい!メカブ!") $ python test_2.py
18.
今後の展開 ▪ クラスタに、自分の発言の精神状態をつけてあげる ▪ (教師あり学習) ▪
自分の発言の精神状態を分かるようにする? ▪ Beagle Black Bone(ARM)でscikitが使えれば=>最 新tweet取ってきて精神状態によりLEDを点滅さす か?
19.
完全にひとり遊びです。 だれも楽しくない!!--;
20.
caffeの紹介 http://caffe.berkeleyvision.org/
21.
個人で等身大のMLをやる場合の 障害 ▪ 学習させるための自前データない! ▪ オープンデータを使うくらい? ▪
画像であれば1判断(ネコかどうかを判断するためのネ コ画像) につき8000件は欲しいよ ▪ 画像は最適化の加工が大変すぎる(OpenCVで顔認識 等を使ったとしても)
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