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✔ Train large-scale semantic NLP models ✔ Represent text as semantic vectors ✔ Find semantically related documents from gensim import corpora, models, similarities, downloader # Stream a training corpus directly from S3. corpus = corpora.MmCorpus("s3://path/to/corpus") # Train Latent Semantic Indexing with 200D vectors. lsi = models.LsiModel(corpus, num_topics=200) # Convert another corpus t
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Welcome to the VFML (Very Fast Machine Learning) toolkit for mining high-speed data streams and very large data sets. VFML is made up of three main components. The first is a collection of tools and APIs that help a user develop new learning algorithms. The second component is a collection of implementations of important learning algorithms. The third component is a collection of scalable learning
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No free lunch in search and optimization - Wikipedia
This article is about mathematical analysis of computing. For associated folklore and broad implications of the theorem, see No free lunch theorem. The problem is to rapidly find a solution among candidates a, b, and c that is as good as any other, where goodness is either 0 or 1. There are eight instances ("lunch plates") fxyz of the problem, where x, y, and z indicate the goodness of a, b, and c
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Machine Learning video segments by topic - Professor Yaser Abu-Mostafa Aggregation Overview of ensemble learning (boosting, blending, before and after the fact) Bayesian Learning Validity of the Bayesian approach (prior, posterior, unknown versus probabilistic) Bias-Variance Tradeoff Basic derivation (overfit and underfit, approximation-generalization tradeoff) Example (sinusoidal target function)
第17回 データマイニング+WEB 勉強会＠東京( #TokyoWebmining 17th)−分散機械学習・ビジネス展開 祭り− を開催しました - hamadakoichi blog
2012/05/20 "第17回 データマイニング+WEB 勉強会＠東京−分散機械学習・ビジネス展開 祭り−"を開催しました。 第17回 データマイニング+WEB 勉強会＠東京 ( #TokyoWebmining 17th)−分散機械学習・ビジネス展開 祭り−: Eventbrite Google グループ 会場提供し運営を手伝って下さった ニフティ株式会社 のみなさん、どうもありがとうございました。素敵なトークを提供してくれた講師メンバーに感謝します。会場参加、USTREAM参加ともに多くの方々の参加を嬉しく思っています。 参加者ID・バックグラウンド一覧： 参加者Twitter List: Twitter List TokyoWebmining#17 (@horihorioさんに感謝） 以下、全講師資料、関連資料、ツイートまとめです。 AGENDA： ■Opening Talk： O1
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導入pdf 情報の変換過程のモデル化 ベイズ統計の意義 識別モデルと生成モデル 最尤推定、MAP推定 データの性質 情報理論の諸概念 (KL-divergenceなど) 距離あるいは類似度 数学のおさらいpdf 行列の微分 線形代数学の役立つ公式 多次元正規分布 条件付き正規分布 Bayes推論pdf Bayseによる確率分布推定の考え方 多項分布、ディリクレ分布 事前分布としてのディリクレ分布の意味<\li> 正規分布と事後分布 指数型分布族 自然共役事前分布の最尤推定 線形回帰および識別pdf 線形回帰のモデル 正則化項の導入 L2正則化 L1正則化 正則化項のBayes的解釈 線形識別 2乗誤差最小化の線形識別の問題点 生成モデルを利用した識別 学習データと予測性能pdf 過学習 損失関数と Bias,Variance, Noise K-Nearest Neighbor法への応用 b
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Stanford Machine Learning The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. The topics covered are shown below, although for a more detailed summary see lecture 19. The only content not covered here is the Octave/MATLAB programmi
Hashing Representations
I've discovered that there are many 'hashing tricks' in machine learning. Some of these are like the count-min sketch in that they rely on an explicit bloom filter style datastructure. The ones here take a radical conceptual step: they only use one hashing function. This turns out to work out remarkably well when learning, because the learning algorithm can learn to deal with collisions. In severa
Apache MADlib
Apache MADlib: Big Data Machine Learning in SQL Open source, commercially friendly Apache license For PostgreSQL and Greenplum Database® Powerful machine learning, graph, statistics and analytics for data scientists Getting Started with Apache MADlib using Jupyter Notebooks We have created a library of Jupyter Notebooks to help you get started quickly with MADlib. It includes many commonly used al
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