## page was renamed from 機械学習 #pragma section-numbers off [[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 = ---- CategoryTechnical