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ã‚ªãƒ¼ãƒ—ãƒ³ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆãŒå¿…è¦ã æ©Ÿæ¢°å¦ç¿’ã¯æ•°å¤šãã®ã‚³ãƒŸãƒ¥ãƒ‹ãƒ†ã‚£ãŒäº¤ã‚ã‚Šã¤ã¤ç™ºå±•ã—ã¦ã„ã¾ã™ã€‚æ©Ÿæ¢°å¦ç¿’ãƒ¢ãƒ‡ãƒ«ã‚’é™ã‚‰ã‚ŒãŸäººå“¡ã‚„æ™‚é–“ã®ä¸ã§å††æ»‘ã«ä½œæˆã™ã‚‹ã«ã¯ã€æƒ…å ±è³‡æºã‚’æœ‰åŠ¹ã«æ´»ç”¨ã™ã‚‹å¿…è¦ãŒã‚ã‚Šã€ã‚ªãƒ¼ãƒ—ãƒ³ã‚½ãƒ¼ã‚¹ã ã‘ã§ã¯ãªãã€ãƒ‡ãƒ¼ã‚¿ã‚‚å†åˆ©ç”¨ã—ã‚„ã™ã„å½¢ã§å…¬é–‹ã™ã‚‹ã‚ªãƒ¼ãƒ—ãƒ³ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã®å¿…è¦æ€§ãŒæ€¥é€Ÿã«é«˜ã¾ã£ã¦ã„ã¾ã™ã€‚ ã—ã‹ã—ã€OSSç²¾ç¥žã‚ãµã‚Œã‚‹é–‹ç™ºè€…ãŒ huggingface ç‰ã®ãƒ—ãƒ©ãƒƒãƒˆãƒ•ã‚©ãƒ¼ãƒ ã‚’ç”¨ã„ã¦ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚’å…¬é–‹ã—ãŸã„ã¨è€ƒãˆã¦ã‚‚ã€æ•°å¤šãã‚ã‚‹ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ã®ä¸ã§ã©ã®ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ã‚’é¸æŠžã™ã‚Œã°è‰¯ã„ã®ã‹ã€ãã‚‚ãã‚‚ä½•ã‚’å…¬é–‹ã—ã¦è‰¯ã„ã®ã‹ã¨ã„ã†ã®ã¯ã€éžå¸¸ã«æ‚©ã¾ã—ã„å•é¡Œã§ã‚ã‚Šã€ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚’å…¬é–‹ã™ã‚‹ãƒãƒ¼ãƒ‰ãƒ«ã¯é«˜ãã‚ã‚Šç¶šã‘ã¦ã„ã¾ã™ã€‚å…¬é–‹ã«è¶³è¸ã¿ã™ã‚‹ãŸã‚ã«ã€ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆãŒã‚³ãƒŸãƒ¥ãƒ‹ãƒ†ã‚£å†…éƒ¨ã§ã®ã¿ä½¿ç”¨ã•ã‚Œã¦ã„ã‚‹ã‚±ãƒ¼ã‚¹ãŒæ•°å¤šãã‚ã‚‹çŠ¶æ³ã‚’ã€ã‚‚ã£ãŸã„ãªã„ã¨æ„Ÿã˜ã€ãã®ã‚ˆã†ãªå•é¡Œæ„è˜ã®ä¸Šã§æœ¬ç¨¿ã‚’åŸ·ç†ã—ã¾ã—ãŸã€‚ æœ¬ç¨¿ã§ã¯ã€ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆå‘ã‘ã®
arrowKato 2024/12/18
ã€€ã€Œã‚ªãƒ¼ãƒ—ãƒ³ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã‚’ã¤ãã‚‹äººã®ãŸã‚ã®ãƒ©ã‚¤ã‚»ãƒ³ã‚¹ã‚¬ã‚¤ãƒ‰ã€ã ãŒã€ä½¿ã†äººã‹ã‚‰ã¿ã¦ã‚‚æœ‰ç›Šã€‚æœ«å°¾ã®å‚è€ƒæ–‡çŒ®ãƒªã‚¹ãƒˆã‚‚å‚ç…§ã•ã‚ŒãŸã—ã€‚

ML

dataset

license
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GitHub - EthicalML/awesome-artificial-intelligence-regulation: This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.
arrowKato 2024/11/18
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ML

AI
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GitHub - marqo-ai/GCL: Generalised Contrastive Learning. This is a Repository for Google Shopping Dataset and Benchmarks followed by our novel fine-grained contrastive learning framework.
arrowKato 2024/10/29
Generalized Contrastive Learning(å¯¾ç…§å¦ç¿’) ãŒæµè¡Œã£ã¦ã„ã‚‹ã‚‰ã—ã„ã€‚å¯¾ç…§å¦ç¿’ã¯ã€æ‰‹å‹•ã«ã‚ˆã‚‹æ³¨é‡ˆä»˜ã‘ã®å¿…è¦æ€§ãŒæœ€å°é™ã§ã‚ã‚‹ãŸã‚ã€æ¤œç´¢ã‚¿ã‚¹ã‚¯ã§åºƒãæŽ¡ç”¨ã•ã‚Œã¦ã„ã¾ã™ã€‚

ML

training
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Evidently AI - ML system design: 300 case studies
How do companies like Netflix, Airbnb, and Doordash apply machine learning to improve their products and processes? We put together a database of 300 case studies from 80+ companies that share practical ML use cases and learnings from designing ML systems. Navigation tips. You can play around with the database by filtering case studies by industry or ML use case. We added tags based on recurring t
arrowKato 2024/10/02
ã„ã£ã±ã„ã‚ã‚‹ã‘ã©ã€LLMã§ç›®æ–°ã—ã„ã‚‚ã®ã¯ãªã„ãªãã€‚ 100 ç¤¾ä»¥ä¸Šã®ä¼æ¥ã‹ã‚‰ã® 450 ä»¶ã®ã‚±ãƒ¼ã‚¹ ã‚¹ã‚¿ãƒ‡ã‚£ã®ãƒ‡ãƒ¼ã‚¿ãƒ™ãƒ¼ã‚¹

ML

MLOps

android
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The Pragmatic Programmer for Machine Learning
The Pragmatic Programmer for Machine Learning Engineering Analytics and Data Science Solutions Marco Scutari, Mauro Malvestio 2023-04-22 Preface Pitching new ideas by prefacing them with quotes like â€œData scientist: the sexiest job of the 21st centuryâ€ (Harvard Business Review 2012) or â€œData is the new oilâ€ (The Economist 2017) has become such a clichÃ© that any audience (in business and academia a
arrowKato 2024/09/24
The Pragmatic Programmer for Machine Learning

DS

ML

MLOps
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How to get from high school math to cutting-edge ML/AI: a detailed 4-stage roadmap with links to the best learning resources that Iâ€™m aware of.
arrowKato 2024/08/21
é«˜æ ¡æ•°å¦ã‹ã‚‰æœ€å…ˆç«¯ã® ML/AI ã«åˆ°é”ã™ã‚‹æ–¹æ³•: ç§ãŒçŸ¥ã‚‹é™ã‚Šæœ€é«˜ã®å¦ç¿’ãƒªã‚½ãƒ¼ã‚¹ã¸ã®ãƒªãƒ³ã‚¯ã‚’å«ã‚€è©³ç´°ãª 4 æ®µéšŽã®ãƒãƒ¼ãƒ‰ãƒžãƒƒãƒ—ã€‚

ML

LLM
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https://moment-timeseries-foundation-model.github.io/
arrowKato 2024/05/13
æ™‚ç³»åˆ—ã®foundation model ã†ã¾ãã„ãã¨ã„ã„ã‘ã©ã€ã‚„ã£ã±ã‚Šè¨€èªžã¨ã‹ç”»åƒã¿ãŸã„ãªå…±é€šç†è§£ãŒãƒ†ãƒ¼ãƒ–ãƒ«ãƒ‡ãƒ¼ã‚¿ã«ã‚ã‚‹ã®ã‹ã©ã†ã‹ã¯çµæ§‹ç–‘å•ã€‚

ML
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AI Index Report 2024 â€“ Artificial Intelligence Index
Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AIâ€™s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the techno logy, and the geopolitical dynamics surroundin
arrowKato 2024/04/26
State of AI 2024/4æœˆç‰ˆ

ML
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What I learned from looking at 900 most popular open source AI tools
[Hacker News discussion, LinkedIn discussion, Twitter thread] Four years ago, I did an analysis of the open source ML ecosystem. Since then, the landscape has changed, so I revisited the topic. This time, I focused exclusively on the stack around foundation models. The full list of open source AI repos is hosted at llama-police. The list is updated every 6 hours. You can also find most of them on
arrowKato 2024/03/20
900ã®AIé–¢é€£GitHubãƒªãƒã‚¸ãƒˆãƒªã‚’èª¿ã¹ã¦ã‚ã‹ã£ãŸã“ã¨

ML
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Neural Networks: Zero To Hero
A course by Andrej Karpathy on building neural networks, from scratch, in code. We start with the basics of backpropagation and build up to modern deep neural networks, like GPT. In my opinion language models are an excellent place to learn deep learning, even if your intention is to eventually go to other areas like computer vision because most of what you learn will be immediately transferable.
arrowKato 2023/11/27
ML
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https://owasp.org/www-project-top-10-for-large-language-model-applications/descriptions/
arrowKato 2023/06/11
LLM

ML

security
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é¡žä¼¼ãƒ™ã‚¯ãƒˆãƒ«æ¤œç´¢ã®SoTAï¼ï¼GCP: Vertex Matching Engineã«ã‚‚ä½¿ç”¨ã•ã‚Œã¦ã„ã‚‹æ‰‹æ³•ScaNNã‚’ç´¹ä»‹ï¼
3ã¤ã®è¦ç‚¹ âœ”ï¸ GCPã®æ–°ãƒ—ãƒãƒ€ã‚¯ãƒˆã€ŒVertex Matching Engineã€ã®å…ƒè«–æ–‡ âœ”ï¸ MIPSã‚¹ã‚³ã‚¢ã‚’è€ƒæ…®ã—ãŸæ–°ã—ã„é‡ååŒ–æå¤±é–¢æ•°ã‚’ææ¡ˆ âœ”ï¸ ANNBenchmarksã§SoTAæ€§èƒ½ã‚’è¨˜éŒ² Accelerating Large-Scale Inference with Anisotropic Vector Quantization written byÂ Ruiqi Guo,Â Philip Sun,Â Erik Lindgren,Â Quan Geng,Â David Simcha,Â Felix Chern,Â Sanjiv Kumar (Submitted on 27 Aug 2019 (v1), last revised 4 Dec 2020 (this version, v5)) Comments:Â Published as a conference paper at ICM
arrowKato 2021/06/26
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ML
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Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks Â· Issue #2057 Â· arXivTimes/arXivTimes
arrowKato 2021/05/12
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å®Ÿå‹™ã«ãŠã‘ã‚‹ãƒ¢ãƒ‡ãƒ«æ›´æ–°ã®è¾›ã¿ã‚’è§£æ±ºï¼ï¼Ÿã€€ãƒãƒƒã‚¯ãƒ•ã‚£ãƒ«ãƒ•ãƒªãƒ¼ã®ãƒ¢ãƒ‡ãƒ«å·®ã—æ›¿ãˆã‚’å®Ÿç¾ã™ã‚‹è¡¨ç¾å¦ç¿’æ‰‹æ³•ãŒç™»å ´ï¼ï¼
3ã¤ã®è¦ç‚¹ âœ”ï¸ CVPR2020ã®oralè«–æ–‡ï¼šå¾Œæ–¹äº’æ›æ€§ã®ã‚ã‚‹ç‰¹å¾´è¡¨ç¾ã‚’å¦ç¿’ã™ã‚‹æ‰‹æ³•ã‚’ææ¡ˆ âœ”ï¸ ãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯æ§‹é€ ã‚„æå¤±é–¢æ•°ã«ã‚‚ä¾å˜ã—ãªã„äº’æ›æ€§åŸºæº–ã®å®šç¾©ã¨å¦ç¿’æ–¹æ³• âœ”ï¸Â ãƒãƒƒã‚¯ãƒ•ã‚£ãƒ«ãªã—ã§ãƒ¢ãƒ‡ãƒ«ã®å·®ã—æ›¿ãˆãŒå¯èƒ½ã« Towards Backward-Compatible Representation Learning written byÂ Yantao Shen,Â Yuanjun Xiong,Â Wei Xia,Â Stefano Soatto (Submitted on 26 Mar 2020 (v1), last revised 29 Mar 2020 (this version, v2)) Comments: Accepted to CVPR 2020 as oral Subjects: Computer Vision and Pattern Recognition (cs.CV)
arrowKato 2021/03/31
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Retty ãƒ‡ãƒ¼ã‚¿åˆ†æžãƒãƒ¼ãƒ 2020å¹´ã®æŒ¯ã‚Šè¿”ã‚Š - æ„æ€æ±ºå®šæ”¯æ´/åˆ†æžæ°‘ä¸»åŒ–/ãƒ‡ãƒ¼ã‚¿åŸºç›¤/ML - Retty Tech Blog
ã“ã®è¨˜äº‹ã¯Retty Advent Calendar 2020ã®21æ—¥ç›®ã®è¨˜äº‹ã§ã™ã€‚ adventar.org æ˜¨æ—¥ã¯ã€æ£®ç”°ã•ã‚“ã®ETLåŸºç›¤ã§ãƒ‡ãƒ¼ã‚¿ã‚’æ±Žç”¨çš„ã«å‡¦ç†ã§ãã‚‹ã‚ˆã†ã«ã—ãŸè©±ã§ã—ãŸã€‚ ã¯ã˜ã‚ã« ã“ã‚“ã«ã¡ã¯ã€‚å¹³é‡Ž(@MasaDoN22)ã§ã™ã€‚ Retty ãƒ‡ãƒ¼ã‚¿åˆ†æžãƒãƒ¼ãƒ ã®ãƒžãƒãƒ¼ã‚¸ãƒ£ãƒ¼ã‚’æ‹…å½“ã—ã¦ã„ã¾ã™ã€‚ åŽ»å¹´ã€ä¸€æ˜¨å¹´ã«å¼•ãç¶šãã€åˆ†æžãƒãƒ¼ãƒ ã®1å¹´ã®æŒ¯ã‚Šè¿”ã‚Šã¨ã—ã¦æ›¸ãã¾ã—ãŸã€‚ ä»Šå¹´ã‚’ä¸€è¨€ã§ã„ã†ã¨ã€æŒã¦ã‚‹æ¦å™¨ã‚’æœ€å¤§é™æ´»ç”¨ã—ã¦ã€ç›®ã®å‰ã®èª²é¡Œã«å‘ãåˆã£ãŸä¸€å¹´ã§ã—ãŸã€‚ å†…å®¹ã¨ã—ã¦ã¯ã€åˆ†æžãƒãƒ¼ãƒ ã®å½¹å‰²ã§ã‚ã‚‹æ„æ€æ±ºå®šæ”¯æ´ãƒ»åˆ†æžæ°‘ä¸»åŒ–ãƒ»ãƒ‡ãƒ¼ã‚¿åŸºç›¤ãƒ»MLã«æ²¿ã£ã¦æ›¸ã„ãŸä¸€å¹´ã®ç·é›†ç·¨ã§ã™ã€‚ ãã®çµæžœã€ä»Šå¹´ã‚‚æ–‡é‡ãŒå¤šããªã£ã¦ã—ã¾ã£ãŸã®ã§ã€èˆˆå‘³ã®ã‚ã‚‹åˆ†é‡Žã ã‘æŠœç²‹ã—ã¦ãŠèªã¿ã„ãŸã ã‘ã¾ã™ã¨å¹¸ã„ã§ã™ã€‚ æœ¬è¨˜äº‹ã®å‰æã¨ãªã‚‹ã€Retty ãƒ‡ãƒ¼ã‚¿åˆ†æžãƒãƒ¼ãƒ ã®å½¹å‰²ã‚„éŽåŽ»ã®å–ã‚Šçµ„ã¿ã¯ã€ä»¥ä¸‹è¨˜äº‹ã‚’å¾¡è¦§ãã ã•ã„ã€‚ engineer.re
arrowKato 2021/03/17
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ML
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æ©Ÿæ¢°å¦ç¿’ PoC ã¨æœ¬ç•ªé‹ç”¨ã®ã‚ã„ã - Qiita
English version is here: (https://koyaaarr.medium.com/between-machine-learning-poc-and-production-618502abef86) ã¯ã˜ã‚ã« æ©Ÿæ¢°å¦ç¿’ã‚’ä½¿ã£ãŸ PoC(Proof of Concept; åŠ¹æžœæ¤œè¨¼)ã¯ã€è¿‘é ƒã® AI ãƒ–ãƒ¼ãƒ ã‚’å—ã‘ã¦ç››ã‚“ã«è¡Œã‚ã‚Œã¦ã„ã‚‹ã“ã¨ã¨æ€ã„ã¾ã™ã€‚ãã—ã¦ãã®å¾Œã€(ã¨ã¦ã‚‚å¹¸é‹ãªã“ã¨ã«ã€)PoC ã§è‰¯ã„çµæžœã‚’å¾—ã‚‰ã‚ŒãŸå ´åˆã€PoC ã®ä»•çµ„ã¿ã‚’æœ¬ç•ªé‹ç”¨ã—ãŸã„ã¨ã„ã†è©±ãŒæŒã¡ä¸ŠãŒã‚Šã¾ã™ã€‚ã—ã‹ã—ãªãŒã‚‰ã€æŽ¢ç´¢çš„ãƒ‡ãƒ¼ã‚¿åˆ†æžã‚„äºˆæ¸¬ãƒ¢ãƒ‡ãƒ«ã®ä½œæˆãªã©ã¯å¤šãã®çŸ¥è¦‹ãŒå…±æœ‰ã•ã‚Œã¦ã„ã‚‹ä¸€æ–¹ã€ãã‚Œã‚‰ã‚’é‹ç”¨ã—ã¦ã„ããŸã‚ã®çŸ¥è¦‹ã¯ã¾ã ã‚ã¾ã‚Šå¤šããªã„ã‚ˆã†ã«æ€ã„ã¾ã™ã€‚ æœ¬è¨˜äº‹ã§ã¯ã€PoC ã‹ã‚‰æœ¬ç•ªé‹ç”¨ã¸ã¨ç§»è¡Œã™ã‚‹ã‚ã„ã ã«ã€æŠ€è¡“çš„ã«ã¯ä½•ãŒå¿…è¦ãªã®ã‹ã‚’æ¤œè¨Žã—ã¦ã„ãã¾ã™ã€‚æ©Ÿæ¢°å¦ç¿’ PoC ãŒä¸€éŽæ€§ã®ã¾ã¾çµ‚ã‚ã‚‰ãšã€æœ¬ç•ª
arrowKato 2021/01/31
PoCã‹ã‚‰æœ¬ç•ªé‹ç”¨ã¾ã§ã«å¿…è¦ãªã“ã¨

MLOps

ML
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https://cloudonair.withgoogle.com/events/google-mlsummit
arrowKato 2021/01/23
GCPã® MLSummit2020ã®å…¬é–‹è³‡æ–™ä¸€è¦§ã®ãƒªãƒ³ã‚¯

ML

MLOps

GCP
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GitHub - ml-tooling/best-of-ml-python: ðŸ† A ranked list of awesome machine learning Python libraries. Updated weekly.
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arrowKato 2021/01/20
ãƒ„ãƒ¼ãƒ«ä¸€è¦§

ML

MLOps
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Papers with Code 2020 Review
Papers with Code indexes various machine learning artifacts â€” papers, code, results â€” to facilitate discovery and comparison. Using this data we can get a sense of what the ML community found useful and interesting this year. Below we summarize the top trending papers, libraries and benchmarks for 2020 on Papers with Code. Top Trending Papers of 2020 EfficientDet by Tan et al was the most viewed p
arrowKato 2021/01/04
2020ã§ã®å„ãƒ‡ãƒ¼ã‚¿ã‚»ãƒƒãƒˆã”ã¨ã®ç²¾åº¦ãŒé«˜ã„é †ã®æ‰‹æ³•ã¾ã¨ã‚

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ML
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ml-system-design-pattern/README_ja.md at master Â· mercari/ml-system-design-pattern
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arrowKato 2021/01/03
MLã‚’å«ã‚€ã‚·ã‚¹ãƒ†ãƒ ã®ã‚¢ãƒ¼ã‚ãƒ†ã‚¯ãƒãƒ£ã®ãƒ‡ã‚¶ã‚¤ãƒ³ãƒ‘ã‚¿ãƒ¼ãƒ³

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MLOps
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