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Companion webpage to the book "Mathematics for Machine Learning"
Companion webpage to the book "Mathematics for Machine Learning". Copyright 2020 by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Published by Cambridge University Press. View the Project on GitHub View On GitHub Please link to this site using https://mml-book.com. Twitter: @mpd37, @AnalogAldo, @ChengSoonOng. We wrote a book on Mathematics for Machine Learning that motivates people to
Simulating animal movements and habitat use
Hi there! I was training some ways to simulate animal (or other organisms) movements having into account habitat suitability. To do this, I used my previous eWalk model as the underlying process to simulate random or directional walks. This model is based on Brownian / Ornstein–Uhlenbeck process. You can find more about eWalk model here! Today, I will add one more element to this movement simulati
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bayesopt <- function(target_func, ..., iter = 10, kernel = c("matern5_2", "matern3_2", "square_exp"), acq_func = c("mutual_information", "confidence_bound"), k = 2) { if(!require(GPfit)) stop('install.packages("GPfit")') # Prepare ----------------------------------------------------------------- target_func <- match.fun(target_func) kernel <- match.arg(kernel) acq_func <- match.arg(acq_func) param
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RStudioã®ãƒãƒ¼ãƒ•ã‚µã‚¤ã‚¨ãƒ³ãƒ†ã‚£ã‚¹ãƒˆã€Hadley Wickham（ãƒãƒ‰ãƒªãƒ¼ï¼‰ãŒï¼’月ã«è¡Œã£ãŸè¬›æ¼”ã®ãƒ“デオãŒYouTubeã«ä¸ŠãŒã£ã¦ã„ãŸã®ã§è¦³ãŸã€‚ "Making Data Analysis Easier"ã¨ã„ã†ã‚¿ã‚¤ãƒˆãƒ«ã§ã®ç™ºè¡¨(スライドã§ã¯"Managing many models"ã«ãªã£ã¦ã„ã‚‹ã‘ã©)ã§ã€ãƒãƒ‰ãƒªãƒ¼è‡ªèº«ãŒè€ƒãˆã¦ã„ã‚‹ã€ãƒ‡ãƒ¼ã‚¿ã‚µã‚¤ã‚¨ãƒ³ã‚¹ã«å¿…è¦ãªå¯è¦–化やモデリングを効率的ã«è¡Œã†ãŸã‚ã®æ‰‹æ³•ã«ã¤ã„ã¦ã€å½¼ã®é–‹ç™ºã—ã¦ããŸãƒ‘ッケージをä¸å¿ƒã«èª¬æ˜Žã—ã¦ã„る。 www.youtube.com 分ã‹ã‚Šã‚„ã™ãã€å…·ä½“例を交ãˆãŸå†…容ãªã®ã§ã€æ˜¯éžYouTubeã®å‹•ç”»ã‚’観ã¦ã‚‚らã†ã®ãŒè‰¯ã„ã¨æ€ã†ãŒã€è‡ªåˆ†ã®é ã‚’æ•´ç†ã™ã‚‹ãŸã‚ã«ã‚‚ã“ã“ã§ã¾ã¨ã‚ã¦ãŠã。ãªãŠã€ç™ºè¡¨ã‚¹ãƒ©ã‚¤ãƒ‰ã¯ã‚¯ãƒªã‚¨ã‚¤ãƒ†ã‚£ãƒ–・コモンズライセンス3.0ã®ã‚‚ã¨ã€è¡¨ç¤ºãƒ»éžå–¶åˆ©ã®ãƒ©ã‚¤ãƒ³ã‚»ãƒ³ã‚¹ã§å†åˆ©ç”¨å¯èƒ½ã¨ãªã£ã¦ã„る。 Hadley Wickham (Chief S
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概è¦æ¸¡è¾ºæ¾„夫ã®ã€Žãƒ™ã‚¤ã‚ºçµ±è¨ˆã®ç†è«–ã¨æ–¹æ³•ã€ (以下, 渡辺本) ã¯, ç§ã®ã‚ˆã†ã«çµ±è¨ˆå¦ã¯å¤šå°‘知ã£ã¦ã‚‹ã‚‚ã®ã®, 統計力å¦ã‚’知らãªã„人間ã«ã¨ã£ã¦ã¯, 「自由エãƒãƒ«ã‚®ãƒ¼ã€ã ã®ã€Œåˆ†é…関数ã€ã ã®ãŒä½•ã‚’æ„図ã—ã¦å®šç¾©ã•ã‚ŒãŸçµ±è¨ˆé‡ (物ç†é‡?) ãªã®ã‹ã‚ˆãã‚ã‹ã‚‰ãš, ã¯ã˜ã‚ã¯æ•°å¼ã‚’ç›®ã§è¿½ã†ã“ã¨ã—ã‹ã§ããªã‹ã£ãŸ. åŠ ãˆã¦, 渡辺本ã¯, 実務ã«å½¹ã«ç«‹ã¤ãƒ†ã‚¯ãƒ‹ãƒƒã‚¯ãªã©ã¨ã„ã£ãŸè¶£æ—¨ã®æœ¬ã§ã¯ãªã, 統計å¦çš„ãªãƒ¢ãƒ‡ãƒªãƒ³ã‚°ã‚„機械å¦ç¿’*1ã®ç†è«–を統一的ã«èª¬æ˜Žã™ã‚‹ã“ã¨ã‚’目的ã¨ã—ã¦ã„ã‚‹. ãã®ãŸã‚, 統計モデルを抽象的ã«ä¸€èˆ¬åŒ–ã—ã¦ãã®æ€§è³ªã‚’説明ã™ã‚‹ä¸€æ–¹ã§, æ··åˆåˆ†å¸ƒãƒ¢ãƒ‡ãƒ«ã¨ã‹, ニューラルãƒãƒƒãƒˆã¨ã‹å…·ä½“çš„ãªæ‰‹æ³•ã«ã¤ã„ã¦ã®è¨€åŠã¯æŽ§ãˆã‚ã§ã‚ã‚‹ã“ã¨ã‚‚, 人ã«ã‚ˆã£ã¦ã¯ç†è§£ãŒé€²ã¾ãªã„åŽŸå› ã«ãªã£ã¦ã„ãã†ã§ã‚ã‚‹. ã—ã‹ã—, 難解ã§ã‚ã£ã¦ã‚‚, 渡辺本ã«æ›¸ã‹ã‚Œã¦ã„る内容ã¯éžå¸¸ã«ä¾¡å€¤ãŒã‚ã‚‹. 例ãˆã°, 以å‰ç§ãŒ [æ•™æ] 今更ã ãŒ, ベイズ統計ã¨
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