⊕ By Fabian Pedregosa. Category: misc #machine learning #logistic regression #Python #SciPy Mon 20 May 2013 In this post I compar several implementations of Logistic Regression. The task was to implement a Logistic Regression model using standard optimization tools from scipy.optimize and compare them against state of the art implementations such as LIBLINEAR. In this blog post I'll write down all

✔   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

15-712 Advanced and Distributed Operating Systems, Fall 2023 Overview 15-712 is a PhD course in operating systems. The goals are: To understand the state of the art in operating systems and distributed systems, and how and why we got there. To understand how to engage in systems research. To investigate novel ideas in systems through a semester-long research project. Please note: This course is op

The Berkeley/Stanford Recovery-Oriented Computing (ROC) Project The Recovery-Oriented Computing (ROC) project is a joint Berkeley/Stanford research project that is investigating novel techniques for building highly-dependable Internet services. Read an overview of our research into Recovery-Oriented Computing. Quick navigate to: [ Research | People | Publications | Talks | Retreats | Internal ] RO

If you want to donate with Paypal, my contact address is tuomoster AT gmail DOT com. Amazon Wish List file: vendu project: wizard code Downloads Download Draft #4 References Wizard Code on Hacker News Wizard Code on Reddit Note that I keep updating the book without necessarily changing the draft number so please re-download the file now and again if you are interested. :) Help Needed If you like t

GPS: A Graph Processing System Overview GPS is an open-source system for scalable, fault-tolerant, and easy-to-program execution of algorithms on extremely large graphs. GPS is similar to Google’s proprietary Pregel system, and Apache Giraph. GPS is a distributed system designed to run on a cluster of machines, such as Amazon's EC2. In systems such as GPS and Pregel, the input graph (directed, pos

Department of Computer Science University of California, Irvine Abstract In this paper we study how to efficiently perform set-similarity joins in parallel using the popular MapReduce framework. We propose a 3-stage approach for end-to-end set-similarity joins. We take as input a set of records and output a set of joined records based on a set-similarity condition. We efficiently partition the dat

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

An Introduction Concurrency A property of systems whose global state is composed of the interleaved execution of independent or partially-independent components. Parallelism Parallel systems exhibit concurrency but one or more components may execute at the same time. Concurrent Data Structures A data structure that is shared by cooperating processes. Concurrent execution of operations may render a

（報道発表資料） 2013年2月13日 世界最速のグラフデータ分析処理技術を開発 ～日本の人口規模のソーシャルグラフを3分で分析可能に～ 日本電信電話株式会社（本社：東京都千代田区、代表取締役社長：鵜浦博夫、以下 NTT）は、ビッグデータ分析の新たな潮流であるグラフデータ分析処理※1において、従来技術を数十倍以上高速化する世界最高速の分析技術を開発しました。 本技術によりこれまで膨大な時間を必要とした日本の人口規模のソーシャルグラフ※2をわずか3分で分析可能となります。また、世界最大規模の10億ノードのグラフデータを1時間で分析する事も可能となります。これは友人関係を表すソーシャルグラフ・実世界における様々なグラフデータから、最新データを即座に反映したコミュニケーション支援やリコメンドサービスを提供できることを示しています。今後、本技術の適用領域の拡大を通して医療・公共・社会科学分野へ利用

Peter Bailis :: Highly Available, Seldom Consistent Data management, distributed systems, and beyond HAT, not CAP: Introducing Highly Available Transactions 05 Feb 2013 tl;dr: Highly Available Transactions show it’s possible to achieve many of the transactional guarantees of today’s databases without sacrificing high availability and low latency. CAP and ACID Distributed systems designers face har

January 23, 2013 Volume 11, issue 1 PDF Hazy: Making it Easier to Build and Maintain Big-data Analytics Racing to unleash the full potential of big data with the latest statistical and machine-learning techniques. Arun Kumar, Feng Niu, and Christopher Ré, Department of Computer Sciences, University of Wisconsin-Madison The rise of big data presents both big opportunities and big challenges in doma

[リンク集] 国語施策情報システム 形態素解析システム 茶筌 平成疑問かなづかひ [更新履歴] ２００８年０８月０１日 : 学年別配当漢字と旧字体からの減少画数 ２００８年０１月２９日 : ローマ字入力を整理する -JIS X 4063について ２００８年０１月１９日 : Unicode番号表、文字鏡番号表をFireBoxで動作確認 ・ ２００８年０１月１２日 : MS-IME ローマ字仮名変換表にベンチマーク機能を追加 ・ ２００８年０１月０２日 : MS-IME ローマ字仮名変換表 - 作成支援道具 ・ ２００７年１１月２４日 : 学年別配当漢字と平均画数 ・ ２００７年１１月０３日 : 漢字音読道具が簡体字に対応 ・ ２００７年１０月２７日 : 常用漢字表 - 筆順表 に JIS X 0213の情報を追加 ・ ２００７年１０月２０日 : 文字符号値参照道具、

Probabilistic programming is a newer way of posing machine learning problems. As the models we want to create become more complex it will be necessary to embrace more generic tools for capturing dependencies. I wish to argue that probabilistic programming languages should be the dominant way we perform this modeling, and will demonstrate it by showing the variety of problems that can be trivially