Kyoto Univ. Smart Kitchen Dataset | – Kyoto University Smart Kitchen Dataset –
KUSK Dataset – Kyoto University Smart Kitchen Dataset- æ¦‚è¦ Kyoto University Smart Kitchen dataset (KUSK dataset) ã¯ã‚ッãƒãƒ³ã§ã®äººé–“ã®æ´»å‹•ã‚’観測ã—ãŸãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã§ã™ï¼Ž 本プãƒã‚¸ã‚§ã‚¯ãƒˆã¯èª¿ç†æ”¯æ´ã‚’é¡Œæã¨ã—ã¦ï¼ŒITテクノãƒã‚¸ãƒ¼ã«ä¸æ…£ã‚Œãªãƒ¦ãƒ¼ã‚¶ã«ã‚‚利用å¯èƒ½ãªã‚¤ãƒ³ã‚¿ãƒ¼ãƒ•ã‚§ã‚¤ã‚¹ã®é–‹ç™ºã‚’目指ã—ã¦ã„ã¾ã™ï¼Ž ãã®ã‚ˆã†ãªã‚·ã‚¹ãƒ†ãƒ を実ç¾ã™ã‚‹ãŸã‚ã«ã¯ï¼Œã‚·ã‚¹ãƒ†ãƒ ã¯ãƒ¦ãƒ¼ã‚¶ã®æŒ¯ã‚‹èˆžã„ã‚’å—å‹•çš„ã«è¦³æ¸¬ã—，æ„図を予測ã—，ãã—ã¦ï¼Œã‚¿ã‚¤ãƒ リーã§ï¼Œã‹ã¤ï¼Œé©åˆ‡ãªæ”¯æ´ã‚’æä¾›ã™ã‚‹å¿…è¦ãŒã‚ã‚Šã¾ã™ï¼Ž ã“ã®ã‚ˆã†ãªæ”¯æ´ã®å®Ÿç¾ã‚’ã‚ã–ã—，本データベースã§ã¯ITã®çŸ¥è˜ãŒå¿…è¦ãªã‚¦ã‚§ã‚¢ãƒ©ãƒ–ルセンサã§ã¯ãªã，環境埋ã‚è¾¼ã¿åž‹ã®ã‚»ãƒ³ã‚µã‚’利用ã—ã¦ã„ã¾ã™ï¼Ž 本データベースã¯ç ”究目的ã«é™ã‚Šè‡ªç”±ã«åˆ©ç”¨å¯èƒ½ã§ã™ãŒï¼Œè«–æ–‡ç‰ã§åˆ©ç”¨ã™ã‚‹å ´åˆã«ã¯å‚考文献ã¸ã®å¼•ç”¨ãŒå¿…è¦
Why dont you_create_new_spark_jl
1. Why don't you create new Spark.jl? 2015å¹´7月11æ—¥ JuliaTokyo#4 @sfchaos 1 2. 自己紹介 â– twitterID： @sfchaos â– ã‚ーワード： データマイニング，機械å¦ç¿’，オントãƒã‚¸ãƒ¼ï¼Œ R，Python，Perl，C++，Julia ■今日ã¯ã“ã®è¨€è‘‰ã‚’言ã†ãŸã‚ã«ã‚„ã£ã¦æ¥ã¾ã—ãŸï¼Ž “Why don't you create Spark.jl?†2
FastML
The paper, X-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs, with Yann LeCun as the last author, is about similarity signals between samples, typically images. The point is to learn good representations (embeddings) by contrasting similar and dissimilar images. Today the implied probability of Kamala Harris being elected as the president of the United States
Vowpal Wabbit, Liblinear/SBM and StreamSVM compared - FastML
Our Indiegogo campaign turned out to be a (partial) success, so we deliver as promised: a comparison of Vowpal Wabbit, Liblinear/SBM and StreamSVM on the webspam dataset. Refer to the Comparing large-scale linear learners for motivation and references. Just to recap: the Liblinear/SBM and StreamSVM papers deal with linear learners able to handle data which doesn’t fit into memory. Among other thin
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æ±èŠãŒã€ä¸é©åˆ‡ãªä¼šè¨ˆå‡¦ç†ã®ç™ºè¦šã§ã“ã¨ã—３月期ã®æ±ºç®—ãŒç™ºè¡¨ã§ããªã„異例ã®äº‹æ…‹ã¨ãªã£ã¦ã„ã‚‹å•é¡Œã§ã€ç”°ä¸ä¹…雄社長ãŒä¼šç¤¾ã®å¹¹éƒ¨ã«å¯¾ã—ã€è²»ç”¨ã®è¨ˆä¸Šã‚’å…ˆé€ã‚Šã—ã¦åˆ©ç›Šã®ã‹ã•ä¸Šã’を促ã™è¶£æ—¨ã®ãƒ¡ãƒ¼ãƒ«ã‚’é€ã£ã¦ã„ãŸã“ã¨ãŒé–¢ä¿‚者ã¸ã®å–æã§åˆ†ã‹ã‚Šã¾ã—ãŸã€‚ã“ã®å•é¡Œã‚’調査ã—ã¦ã„る第三者委員会ã¯çµŒå–¶ãƒˆãƒƒãƒ—ã®ä¸é©åˆ‡ãªä¼šè¨ˆå‡¦ç†ã¸ã®é–¢ä¸Žã‚’示ã™ã¨ä½ç½®ã¥ã‘ã¦ã„ã‚‹ã¨è¦‹ã‚‰ã‚Œã€æ±èŠã¯çµŒå–¶é™£ã®è²¬ä»»ã«ã¤ã„ã¦åŽ³ã—ã„判æ–を迫られãã†ã§ã™ã€‚ 関係者ã«ã‚ˆã‚Šã¾ã™ã¨ã€ç¬¬ä¸‰è€…委員会ã«ã‚ˆã‚‹ã“ã‚Œã¾ã§ã®èª¿æŸ»ã§ã€ç”°ä¸ç¤¾é•·ã¯ã€ã‚¤ãƒ³ãƒ•ãƒ©å·¥äº‹ã®äº‹æ¥ãªã©ã§ã€ä¼šç¤¾å¹¹éƒ¨ã«å¯¾ã—ã€è²»ç”¨ã®è¨ˆä¸Šã‚’次ã®å¹´åº¦ã«å…ˆé€ã‚Šã—ã¦ã€åˆ©ç›Šã®ã‹ã•ä¸Šã’を促ã™è¶£æ—¨ã®ãƒ¡ãƒ¼ãƒ«ã‚’é€ã£ã¦ã„ãŸã“ã¨ãŒåˆ†ã‹ã‚Šã¾ã—ãŸã€‚ ã¾ãŸã€ä½ã€…木則夫副会長ã¯ã€ç¤¾é•·ã‚’å‹™ã‚ã¦ã„ãŸå½“時ã€æ¯Žæœˆã€é–‹ã‹ã‚Œã‚‹å¹¹éƒ¨ä¼šè°ã®å ´ã§ã€æ¥ç¸¾ç›®æ¨™ã®é”æˆã‚’å¼·ã指示ã™ã‚‹ç™ºè¨€ã‚’ç¹°ã‚Šè¿”ã—ã¦ã„ãŸã“ã¨ã‚‚分ã‹ã‚Šã¾ã—ãŸã€‚ 第三者委員会ã«ã‚ˆã‚‹èžãå–ã‚Šã«å¯¾ã—ã¦ã€ç”°ä¸ç¤¾é•·ã¨ä½ã€…木副
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GitHub - mrkn/typical_colors: Extracting typical colors from images
GitHub - rauchg/slackin: Public Slack organizations made easy
GitHub - dfdx/Boltzmann.jl: Restricted Boltzmann Machines in Julia
GitHub - Andy-P/DeepQLearning.jl: Julia implementation of DeepMind's Deep Q Learning algorithm described in "Playing Atari with Deep Reinforcement Learning"
GitHub - johnmyleswhite/StreamStats.jl: Compute statistics over data streams in pure Julia
GitHub - zenna/Sigma.jl: Sigma is a probabilistic programming environment implemented in Julia
GitHub - dpsanders/invitation_to_julia
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