## page was renamed from 機械学習
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[[TableOfContents]]

= 概要 =
機械学習

= 関連ライブラリ =
 * ["NumPy"]
 * ["scikit-learn"]
 * ["word2vec"]

= アルゴリズム等 =
 * [http://hivecolor.com/id/65 tfidf、LSI、LDAの違いについて調べてみた - Hive Color]
 * [http://hivecolor.com/id/88 tfidf, lsi, ldaを使ったツイッターユーザーの類似度計算 - Hive Color]

= Deep Learning =
 * [http://www.deeplearning.net/tutorial/ Deep Learning Tutorials — DeepLearning 0.1 documentation]
 * [http://www.slideshare.net/pfi/deep-learning-22350063 一般向けのDeep Learning]
 * [http://blog.yusugomori.com/post/42244843471/python-deep-learning-denoising-autoencoders PythonによるDeep  Learningの実装（Denoising Autoencoders 編） - Yusuke Sugomori's Blog]
 * [http://www.slideshare.net/KentaOono/20141209sigmodj Deep Learning技術の最近の動向とPreferred Networksの取り組み]
 * [http://www.slideshare.net/nlab_utokyo/deep-learning-40959442 Deep Learningと画像認識～歴史・理論・実践～]

= scikit-learn =
[http://scikit-learn.org/stable/tutorial/machine_learning_map/ Choosing the right estimator — scikit-learn 0.15.2 documentation]

[http://www.mwsoft.jp/programming/numpy/logistic_regression.html scikit-learnでlogistic regression | mwSoft]

[http://stackoverflow.com/questions/15564410/scikit-learn-svm-how-to-save-load-support-vectors python - scikit learn SVM, how to save/load support vectors? - Stack Overflow]


= 参考サイト =
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CategoryTechnical