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ML and NLP Research Highlights of 2020
ML and NLP Research Highlights of 2020 This post summarizes progress in 10 exciting and impactful directions in ML and NLP in 2020. The selection of areas and methods is heavily influenced by my own interests; the selected topics are biased towards representation and transfer learning and towards natural language processing (NLP). I tried to cover the papers that I was aware of but likely missed m
AIãŠã‚ˆã³é«˜åº¦IT人æ育æˆã®ãŸã‚ã®æ•™ææ供を開始 - æ ªå¼ä¼šç¤¾Preferred Networks
AIãŠã‚ˆã³é«˜åº¦IT人æ育æˆã®ãŸã‚ã®æ•™ææ供を開始大å¦ç”Ÿãƒ»ç¤¾ä¼šäººå‘ã‘ã«ã€æ©Ÿæ¢°å¦ç¿’・深層å¦ç¿’ã®åŸºç¤Žå¦ç¿’コンテンツ4種をSIGNATE Questã®ãƒžãƒ¼ã‚±ãƒƒãƒˆãƒ—レイスã«å…¬é–‹ æ ªå¼ä¼šç¤¾Preferred Networks（本社：æ±äº¬éƒ½åƒä»£ç”°åŒºã€ä»£è¡¨å–ç· å½¹ 最高経営責任者：西å·å¾¹ã€ãƒ—リファードãƒãƒƒãƒˆãƒ¯ãƒ¼ã‚¯ã‚¹ã€ä»¥ä¸‹ã€PFN）ã¯ã€AIãŠã‚ˆã³ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ã‚¹ã‚’基礎ã‹ã‚‰å¦ã³ãŸã„大å¦ç”Ÿãƒ»ç¤¾ä¼šäººå‘ã‘ã«ã€æ©Ÿæ¢°å¦ç¿’・深層å¦ç¿’ã®åŸºç¤Žå¦ç¿’コンテンツ4種をã€å€‹äººå‘ã‘オンラインAI人æ育æˆè¬›åº§ SIGNATE Quest*ã®ãƒžãƒ¼ã‚±ãƒƒãƒˆãƒ—レイスã§æœ¬æ—¥æ供開始ã—ã¾ã™ã€‚ å„産æ¥ã®å°‚門分野ã«AIãŠã‚ˆã³ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ã‚¹ã‚’応用ã™ã‚‹ã“ã¨ãŒã§ãる人æã®å¤§å¹…ãªä¸è¶³ãŒæŒ‡æ‘˜ã•ã‚Œã‚‹ä¸ã€ãã†ã—ãŸäººæã®è‚²æˆãŒå›½å®¶æˆ¦ç•¥ã®é‡è¦ãƒ†ãƒ¼ãƒžã®ï¼‘ã¤ã¨ã—ã¦ä½ç½®ã¥ã‘られã¦ã„ã¾ã™ã€‚ PFNã¯æ·±å±¤å¦ç¿’フレームワークã®é–‹ç™ºã€æ·±å±¤å¦ç¿’技術ã®ç”£æ¥å¿œç”¨ã«ãŠã„ã¦åŸ¹ã£ã¦ããŸçµŒé¨“ã‚’ã‚‚ã¨
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本記事ã¯MLOps Advent Calendar 2020ã® 2 日目ã®è¨˜äº‹ã§ã™ã€‚ WebAssembly（Wasm）ã¯æ©Ÿæ¢°å¦ç¿’モデルをデプãƒã‚¤ã™ã‚‹æ–°ãŸãªæ‰‹æ®µã«ãªã‚Šã†ã‚‹ã§ã—ょã†ã‹ã€‚ ã“ã®è¨˜äº‹ã§ã¯ã€æ©Ÿæ¢°å¦ç¿’モデル（特㫠Deep Learning）を Wasm ã§ãƒ‡ãƒ—ãƒã‚¤ã™ã‚‹å‘¨è¾ºæŠ€è¡“ã®å‹•å‘や内部ã®ä»•çµ„ã¿ã‚’ã–ã£ãã‚Šã¨èª¬æ˜Žã—ã¾ã™ã€‚ Table of Contents tkat0 ã§ã™ã€‚WebAssembly(Wasm)é¢ç™½ã„ã§ã™ã。 ç§ã‚‚最近ã¯yewã§ãƒ•ãƒãƒ³ãƒˆã‚¨ãƒ³ãƒ‰ã‚’書ã„ã¦éŠã‚“ã§ã„ã¾ã™ã€‚Rust 㧠React ã£ã½ã„ã“ã¨ã§ãã‚‹ã®ã¯æ–°é®®ã§é¢ç™½ã„ã§ã™ã€‚ Wasm ã¯ã€ãªã‚“ã¨ãªã速ㄠJavaScript？ã¨ã‹æ©Ÿæ¢°å¦ç¿’ã§ä½•ã«å½¹ç«‹ã¤ã®ï¼Ÿã¨ã‹æ€ã£ã¦ã‚‹æ–¹ã‚‚多ã„ã¨æ€ã„ã¾ã™ã€‚ ã—ã‹ã—ã€Wasm ã¯ãƒ–ラウザã§ã®æŽ¨è«–時ã«ä½¿ãˆã‚‹ã ã‘ã§ãªãã€æ©Ÿæ¢°å¦ç¿’モデルã®ã‚µãƒ¼ãƒ“ングやエッジデãƒã‚¤ã‚¹ã§ã®æŽ¨è«–ã«ã‚‚使ãˆã‚‹ã¨çŸ¥ã£ãŸã‚‰é©šã
AI Roadmap
i.am.ai AI Expert Roadmap Roadmap to becoming an Artificial Intelligence Expert in 2022 Below you find a set of charts demonstrating the paths that you can take and the technologies that you would want to adopt in order to become a data scientist, machine learning or an AI expert. We made these charts for our new employees to make them AI Experts but we wanted to share them here to help the commun
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ã¯ã˜ã‚㫠最近ã¤ã„ã«ã€Google Meet ã«èƒŒæ™¯ã¼ã‹ã—機能ãŒåˆ©ç”¨å¯èƒ½ã«ãªã‚Šã¾ã—ãŸã‚ˆã。日本語ã ã¨ã‚¤ãƒ³ãƒ—レスã®ã‚±ãƒ¼ã‚¿ã‚¤ Watchã®è¨˜äº‹ãªã©ã§ç´¹ä»‹ã•ã‚Œã¦ã¾ã™ã€‚確㋠2020 å¹´ 9 月末å‰å¾Œã§é †æ¬¡ãƒªãƒªãƒ¼ã‚¹ã•ã‚Œã¦ã„ãŸã¨è¨˜æ†¶ã—ã¦ã„ã¾ã™ã€‚ ã“ã®ã¨ãã¯ã€ŒèƒŒæ™¯ã¼ã‹ã—ã€ã®æ©Ÿèƒ½ã—ã‹ãªã‹ã£ãŸã®ã§ã™ãŒã€æœ€è¿‘（ç§ãŒæ°—ã¥ã„ãŸã®ã¯ 2020/10/30）更ã«ã‚¢ãƒƒãƒ—デートã•ã‚Œã¾ã—ãŸã€‚アップデートã§ã€ŒèƒŒæ™¯å·®ã—替ãˆã€æ©Ÿèƒ½ãŒä»˜ã„ã¦ã€ã¼ã‹ã—機能もã¼ã‹ã—効果ãŒå¼·å¼± 2 ã¤ã‹ã‚‰é¸ã¹ã‚‹ã‚ˆã†ã«ãªã‚Šã¾ã—ãŸã€‚ã¾ã 日本語ã®ãƒ‹ãƒ¥ãƒ¼ã‚¹è¨˜äº‹ã¯è¦‹ã¦ãªã„ã§ã™ãŒã€Googleã«ã‚ˆã‚‹ã‚¢ãƒƒãƒ—デートã®ç™ºè¡¨ã¯ã¡ã‚ƒã‚“ã¨ã•ã‚Œã¦ã„ã¾ã™ã€‚ ãã—ã¦ã€Google AI Blog ã§Background Features in Google Meet, Powered by Web MLã¨ã„ã†è¨˜äº‹ãŒå…¬é–‹ã•ã‚Œã€å®Ÿè£…ã«ã¤ã„ã¦ã®è§£èª¬ãŒã•ã‚Œã¾ã—ãŸã€‚ ã“ã®è¨˜äº‹ã¯ãã®è§£èª¬è¨˜äº‹ã‚’
GitHub - oracle/tribuo: Tribuo - A Java machine learning library
Tribuo is a machine learning library in Java that provides multi-class classification, regression, clustering, anomaly detection and multi-label classification. Tribuo provides implementations of popular ML algorithms and also wraps other libraries to provide a unified interface. Tribuo contains all the code necessary to load, featurise and transform data. Additionally, it includes the evaluation
Amazon's Machine Learning University is making its online courses available to the public
Machine learning is a field in computational science that analyzes patterns and structures in data to help with learning, reasoning, and decision-making—all without human interaction. Data is the lifeblood of business, and machine learning helps identify signals among the data noise. Machine learning (ML), a subset of artificial intelligence, is at the center of Amazon’s business. It’s used by tea
GitHub - iterative/cml: â™¾ï¸ CML - Continuous Machine Learning | CI/CD for ML
What is CML? Continuous Machine Learning (CML) is an open-source CLI tool for implementing continuous integration & delivery (CI/CD) with a focus on MLOps. Use it to automate development workflows — including machine provisioning, model training and evaluation, comparing ML experiments across project history, and monitoring changing datasets. CML can help train and evaluate models — and then gener
GitHub - dessa-oss/atlas: An Open Source, Self-Hosted Platform For Applied Deep Learning Development
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GitHub - microsoft/ML-For-Beginners: 12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
インクルーシブ ML  | Google Cloud
porkbun.com | parked domain
GitHub - lucidrains/deep-daze: Simple command line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). Technique was originally created by https://twitter.com/advadnoun
GitHub - exadel-inc/CompreFace: Leading free and open-source face recognition system
GitHub - lukasmasuch/best-of-ml-python: 🆠A ranked list of awesome machine learning Python libraries. Updated weekly.
Machine Learning Attack Series: Image Scaling Attacks · Embrace The Red
GitHub - nidhaloff/igel: a delightful machine learning tool that allows you to train, test, and use models without writing code
GitHub - chris-chris/ml-engineer-roadmap: WIP: Roadmap to becoming a machine learning engineer in 2020
GitHub - cortexlabs/cortex at v0.15.1
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