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lectureã«é–¢ã™ã‚‹mrknã®ãƒ–ãƒƒã‚¯ãƒžãƒ¼ã‚¯ (24)

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Crash Course - ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆè‚²æˆã‚¯ãƒ©ãƒƒã‚·ãƒ¥ã‚³ãƒ¼ã‚¹ -
Crash Course - ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆè‚²æˆã‚¯ãƒ©ãƒƒã‚·ãƒ¥ã‚³ãƒ¼ã‚¹ - Home Crash Course - ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆè‚²æˆã‚¯ãƒ©ãƒƒã‚·ãƒ¥ã‚³ãƒ¼ã‚¹ - çµ±è¨ˆæ•°ç†ç ”ç©¶æ‰€ã®äººçš„ãƒªã‚½ãƒ¼ã‚¹ã‚’æœ€å¤§é™ã«æ´»ç”¨ã—ãŸæ•™æã‚’é–‹ç™ºã—ã¾ã—ãŸã€‚ ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆãŒçŸ¥ã‚‹ã¹ãã“ã¨ã‚’ã²ã¨é€šã‚Šæ¦‚è¦³ã™ã‚‹ã€ç´„2.5æ™‚é–“ã®ãƒ“ãƒ‡ã‚ªæ•™æã¨ã—ã¦ã€YouTubeä¸Šã§å…¬é–‹ã—ã¦ã„ã¾ã™ã€‚å„ãƒ¦ãƒ‹ãƒƒãƒˆã®æœ€å¾Œã«ã¯ã€è¬›å¸«ãŒãŠå‹§ã‚ã™ã‚‹æ–‡çŒ®ãƒªã‚¹ãƒˆã‚‚æŽ²è¼‰ã•ã‚Œã¦ã„ã¾ã™ã€‚
mrkn 2016/11/11
lecture

data_science
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Kurasawa's Home Page
èª¤ã‚Šãªã©ã¯ ã¾ã§ã€‚åŽŸå‰‡ï¼Œè¿”ä¿¡ã—ã¾ã›ã‚“ãŒæ‚ªã—ã‹ã‚‰ãšã€‚ ðŸ™‡ 2025å¹´1æœˆ27æ—¥ æ—¥æœ¬ç‰©ç†å¦ä¼šç¬¬30å›žè«–æ–‡è³žå—è³žã€€è³žçŠ¶ H Kurasawa, T. Suda and T. Suzuki, PTEP 2021, 013D02 2023å¹´1æœˆ25æ—¥ PDFãƒ•ã‚¡ã‚¤ãƒ«ã®ãƒªãƒ³ã‚¯å…ˆã‚’ åƒè‘‰å¤§ãƒ¬ãƒã‚¸ãƒˆãƒª ã«å¤‰æ›´
mrkn 2016/10/23
physics

lecture
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A Course in Machine Learning
by Hal DaumÃ© III Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential consumer of machine learning. CIML is a set of introductory materials that covers most major aspects of modern machine learni
mrkn 2016/07/13
machine_learning

lecture
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mrkn 2016/04/07
lecture

mathematics

physics
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Past/Current Courses + + Tomoki Kawahira + +
Tomoki Kawahira / Graduate School of Economics / Hitotsubashi University Home Works papers, notes.. Courses è¬›ç¾©ï½¥æ¼”ç¿’ï½¥è‘—ä½œ Programs online apps Gallery fractal images Contact em ail, access Current Courses â€» ã€€å—è¬›è€…å‘ã‘ã®è¬›ç¾©æƒ…å ±ã¯å¦å†…ã®ã‚³ãƒ¼ã‚¹ã‚¦ã‚§ã‚¢ï¼ˆmanabaï¼‰ã«ã¦æä¾›ã—ã¦ã„ã¾ã™ï¼Ž Full-List of Courses Past Courses 1 ï¼šè¤‡ç´ è§£æž å¤å…¸è§£æžï¼ˆè¤‡ç´ é–¢æ•°è«–ï¼‰ 2024å¹´åº¦æ˜¥å¦æœŸï¼Œä¸€æ©‹å¤§ ï¼ˆã‚·ãƒ©ãƒã‚¹ï¼Œ è¬›ç¾©ãƒŽãƒ¼ãƒˆï¼‰ï¼Ž æ•°å¦å±•æœ› Iã€€ï¼ˆè¤‡ç´ æ•°ã“ã¨ã¯ã˜ã‚ï¼‰ 2013å¹´åº¦å‰æœŸï¼Œåå¤å±‹å¤§ãƒ»1å¹´ç”Ÿå¯¾è±¡ï¼Ž è¤‡ç´ é–¢æ•°è«– 2011å¹´åº¦å‰æœŸï¼Œåå¤å±‹å¤§ãƒ»ç†å¦éƒ¨æ•°ç†å¦ç§‘2å¹´ç”Ÿå¯¾è±¡ï¼Ž ç¾ä»£æ•°
mrkn 2016/01/30
mathematics

lecture
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Tomoki Kawahira / Graduate School of Economics / Hitotsubashi University Back to Courses | Home å¤šæ§˜ä½“ã®åŸºç¤Žã®ã‚ã‚½ ï¼ˆä»®é¡Œï¼‰ å¤šæ§˜ä½“ã®åŸºç¤Žã®ã‚ã‚½ã«ã¤ã„ã¦ç‹¬è‡ªã®è¦–ç‚¹ã§è¬›é‡ˆã—ã¾ã™ï¼Ž ã”æ„Ÿæƒ³ãƒ»ã”æ„è¦‹ãƒ»ã”è¦æœ›ã¯ kawahiraAmath.titech.ac.jp (@ = A) ã¾ã§ã©ã†ãžï¼Ž ï¼ï¼Žã“ã®ãƒŽãƒ¼ãƒˆã«ã¤ã„ã¦ ï¼ˆver.20120509ï¼‰ ï¼‘ï¼Žç·šå½¢ä»£æ•°ã®åŸºç¤Žã®ã‚ã‚½ ï¼ˆver.20170131ï¼‰ ï¼’ï¼Žå¤šå¤‰æ•°å¾®åˆ†ã®åŸºç¤Žã®ã‚ã‚½ ï¼ˆver.20170131ï¼‰ ï¼“ï¼Žä½ç›¸ç©ºé–“ã®åŸºç¤Žã®ã‚ã‚½ ï¼ˆver.20170131ï¼‰ ï¼”ï¼Žå¤šæ§˜ä½“ ï¼ˆver.20170131) ï¼•ï¼ŽæŽ¥ç©ºé–“ ï¼ˆver.20170131) ï¼–ï¼Žä½™æŽ¥ç©ºé–“ ï¼ˆ06-cot.pdf) ï¼—ï¼Žãƒ™ã‚¯ãƒˆãƒ«å ´ï¼ˆæº–å‚™ä¸ï¼‰ ï¼˜ï¼Žãƒ™ã‚¯ãƒˆãƒ«æŸ ï¼™ï¼Žå¾®åˆ†å½¢å¼ ï¼‘ï¼ï¼Žã‚¹ãƒˆãƒ¼
mrkn 2016/01/30
mathematics

manifold

lecture
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Scipy Lecture Notes â€” Scipy lecture notes
One document to learn numerics, science, and data with PythonÂ¶ Tutorials on the scientific Python ecosystem: a quick introduction to central tools and techniques. The different chapters each correspond to a 1 to 2 hours course with increasing level of expertise, from beginner to expert. Release: 2022.1
mrkn 2015/10/27
scipy

lecture
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Stanford University CS231n: Deep Learning for Computer Vision
Course Logistics Lectures: Tuesday/Thursday 12:00-1:20PM Pacific Time at NVIDIA Auditorium. Lecture Videos: Will be posted on Canvas 'Panopto Course Videos' tab shortly after each lecture. These are unfortunately only accessible to enrolled Stanford students. Office Hours: We will be holding a mix of in-person and Zoom office hours. You can find a full list of times and locations on the calendar.
mrkn 2015/10/26
deep_learning

lecture
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Neural networks class - UniversitÃ© de Sherbrooke
These are the videos I used to teach my Neural networks class at UniversitÃ© de Sherbrooke. The videos, along with the slides and research paper references, a...
mrkn 2015/10/26
machine_learning

lecture
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Deep Learning Summer School, Montreal 2015
Deep Learning Summer School, Montreal 201530 Videos Â· Aug 2, 2015 Deep neural networks that learn to represent data in multiple layers of increasing abstraction have dramatically improved the state-of-the-art for speech recognition, object recognition, object detection, predicting the activity of drug molecules, and many other tasks. Deep learning discovers intricate structure in large datasets by
mrkn 2015/10/26
deep_learning

lecture
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Machine Learning
Lectures 4 & 5: Regularizers, basis functions and cross-validation slides.pdf Video 1 Video 2
mrkn 2015/03/06
machine_learning

lecture
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https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF
mrkn 2012/10/21
ieee754

lecture
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mrkn 2011/11/08
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privacy

security

lecture
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http://www.somedalab.c.u-tokyo.ac.jp/
mrkn 2011/10/28
science

dynamics

lecture
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https://cs229.stanford.edu/materials.html
mrkn 2011/08/17
machine_learning

lecture

stanford
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mrkn 2011/06/19
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money

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mrkn 2009/05/05
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study

lecture
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mrkn 2009/03/02
economics

lecture

textbook
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jpmobileã¯ã˜ã‚ã®ä¸€æ© ï¼ˆ1/3ï¼‰ï¼ ï¼ IT
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mrkn 2009/02/24
ã™ã°ã‚‰ã—ã ã‚‰

ruby

rails

jpmobile

darashi

mobile

development

lecture
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The Vega Science Trust - Richard Feynman - Science Videos
Chosen by the New Scientist - best on-line videos 2007. A set of four priceless archival science video recordings from the University of Auckland (New Zealand) of the outstanding Nobel prize-winning physicist Richard Feynman - arguably the greatest science lecturer ever. Although the recording is of modest technical quality the exceptional personal style and unique delivery shine through.
mrkn 2008/01/24
feynman

lecture

physics
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