{"id":14782,"date":"2019-05-26T13:23:03","date_gmt":"2019-05-26T17:23:03","guid":{"rendered":"http:\/\/bangla.salearningschool.com\/recent-posts\/natural-language-processing-resources\/"},"modified":"2019-05-26T13:23:03","modified_gmt":"2019-05-26T17:23:03","slug":"natural-language-processing-resources","status":"publish","type":"post","link":"http:\/\/bangla.sitestree.com\/?p=14782","title":{"rendered":"Natural Language Processing Resources"},"content":{"rendered":"<p>Regular Expressions, Text Normalization, Edit Distance<\/p>\n<p><a href=\"http:\/\/web.stanford.edu\/~jurafsky\/slp3\/2.pdf\">http:\/\/web.stanford.edu\/~jurafsky\/slp3\/2.pdf<\/a><\/p>\n<p>Language Modeling with N-Grams<br \/>\n<a href=\"http:\/\/web.stanford.edu\/~jurafsky\/slp3\/3.pdf\">http:\/\/web.stanford.edu\/~jurafsky\/slp3\/3.pdf<\/a><\/p>\n<p>Slide: <a href=\"https:\/\/web.stanford.edu\/class\/cs124\/lec\/languagemodeling2019.pdf\">https:\/\/web.stanford.edu\/class\/cs124\/lec\/languagemodeling2019.pdf<\/a><\/p>\n<p><a href=\"http:\/\/web.stanford.edu\/~jurafsky\/slp3\/4.pdf\">Chapter 4, &quot;Naive Bayes and Sentiment Classification&quot;<\/a><br \/>\n<a href=\"http:\/\/web.stanford.edu\/~jurafsky\/slp3\/4.pdf\">http:\/\/web.stanford.edu\/~jurafsky\/slp3\/4.pdf<\/a><br \/>\nSlide: <a href=\"https:\/\/web.stanford.edu\/class\/cs124\/lec\/naivebayes.pdf\">https:\/\/web.stanford.edu\/class\/cs124\/lec\/naivebayes.pdf<\/a><\/p>\n<p><a href=\"http:\/\/www.cs.cornell.edu\/home\/llee\/papers\/sentiment.pdf\">Thumbs up? Sentiment Classification using Machine Learning Techniques<\/a><\/p>\n<p><a href=\"http:\/\/www.cs.cornell.edu\/home\/llee\/papers\/sentiment.pdf\">http:\/\/www.cs.cornell.edu\/home\/llee\/papers\/sentiment.pdf<\/a><\/p>\n<p>Lexicons for Sentiment, Affect, and Connotation<br \/>\n<a href=\"http:\/\/web.stanford.edu\/~jurafsky\/slp3\/19.pdf\">http:\/\/web.stanford.edu\/~jurafsky\/slp3\/19.pdf<\/a><\/p>\n<p>Logistic Regression<br \/>\n<a href=\"http:\/\/web.stanford.edu\/~jurafsky\/slp3\/5.pdf\">http:\/\/web.stanford.edu\/~jurafsky\/slp3\/5.pdf<\/a><\/p>\n<p><a href=\"https:\/\/spark-public.s3.amazonaws.com\/cs124\/slides\/ir-1.pdf\">https:\/\/spark-public.s3.amazonaws.com\/cs124\/slides\/ir-1.pdf<\/a><\/p>\n<ul>\n<li><small><a href=\"http:\/\/web.stanford.edu\/~jurafsky\/slp3\/6.pdf\">J+M (3ed) Chapter 6: Vector Semantics, 1-7, 18-26, and review 8-15 (should already be familiar)<\/a>`<\/small><\/li>\n<li><small><a href=\"http:\/\/web.stanford.edu\/~jurafsky\/slp3\/6.pdf\">http:\/\/web.stanford.edu\/~jurafsky\/slp3\/6.pdf<\/a> <\/small><\/li>\n<li><small><br \/>\n<\/small><\/li>\n<li><\/li>\n<\/ul>\n<ul>\n<li><a href=\"http:\/\/web.stanford.edu\/class\/cs124\/p36-weizenabaum.pdf\">Weizenbaum, Joseph. 1966, &quot;ELIZA &#8211; A Computer Program For the Study of Natural Language Communication Between Man And Machine&quot;, Communications of the ACM 9 (1): 36-45<\/a><\/li>\n<li><a href=\"http:\/\/web.stanford.edu\/class\/cs124\/p36-weizenabaum.pdf\">http:\/\/web.stanford.edu\/class\/cs124\/p36-weizenabaum.pdf<\/a><\/li>\n<li><\/li>\n<\/ul>\n<ul>\n<li>Jure Leskovec, Anand Rajaraman, Jeff Ullman. 2014. <a href=\"http:\/\/infolab.stanford.edu\/~ullman\/mmds\/ch9.pdf\">Mining of Massive Datasets. Chapter 9<\/a> 2nd edition. pages 307-311 (intro and 9.1) and 321-327 (9.3).<\/li>\n<li><a href=\"http:\/\/infolab.stanford.edu\/~ullman\/mmds\/ch9.pdf\">http:\/\/infolab.stanford.edu\/~ullman\/mmds\/ch9.pdf<\/a><\/li>\n<li><\/li>\n<li><\/li>\n<li>\n<h5>Web graphs, Links, and PageRank Videos<\/h5>\n<p><a href=\"https:\/\/spark-public.s3.amazonaws.com\/cs124\/slides\/web.pdf\">https:\/\/spark-public.s3.amazonaws.com\/cs124\/slides\/web.pdf<\/a><\/li>\n<li><\/li>\n<li><\/li>\n<li>\n<ul>\n<li>Networks, Crowds, and Markets: <a href=\"http:\/\/www.cs.cornell.edu\/home\/kleinber\/networks-book\/\">Reasoning About a Highly Connected World<\/a> By David Easley and Jon Kleinberg Cambridge University Press (2010) Chapter 2, Sections 3.1-3.3 and Secs 18.1-18.5<\/li>\n<li><a href=\"http:\/\/www.cs.cornell.edu\/home\/kleinber\/networks-book\/\">http:\/\/www.cs.cornell.edu\/home\/kleinber\/networks-book\/<\/a><\/li>\n<li><\/li>\n<li><\/li>\n<li>Reference:<\/li>\n<li>\n<h1>Course: CS 124: From Languages to Information<\/h1>\n<\/li>\n<li><a href=\"http:\/\/web.stanford.edu\/class\/cs124\/\">http:\/\/web.stanford.edu\/class\/cs124\/<\/a><\/li>\n<\/ul>\n<\/li>\n<li>Sayed Ahmed<\/li>\n<\/ul>\n<p>Linkedin: <a href=\"https:\/\/ca.linkedin.com\/in\/sayedjustetc\">https:\/\/ca.linkedin.com\/in\/sayedjustetc<\/a><\/p>\n<p>Blog: <a href=\"http:\/\/sitestree.com\">http:\/\/sitestree.com<\/a>, <a href=\"http:\/\/bangla.salearningschool.com\">http:\/\/bangla.salearningschool.com<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Regular Expressions, Text Normalization, Edit Distance http:\/\/web.stanford.edu\/~jurafsky\/slp3\/2.pdf Language Modeling with N-Grams http:\/\/web.stanford.edu\/~jurafsky\/slp3\/3.pdf Slide: https:\/\/web.stanford.edu\/class\/cs124\/lec\/languagemodeling2019.pdf Chapter 4, &quot;Naive Bayes and Sentiment Classification&quot; http:\/\/web.stanford.edu\/~jurafsky\/slp3\/4.pdf Slide: https:\/\/web.stanford.edu\/class\/cs124\/lec\/naivebayes.pdf Thumbs up? Sentiment Classification using Machine Learning Techniques http:\/\/www.cs.cornell.edu\/home\/llee\/papers\/sentiment.pdf Lexicons for Sentiment, Affect, and Connotation http:\/\/web.stanford.edu\/~jurafsky\/slp3\/19.pdf Logistic Regression http:\/\/web.stanford.edu\/~jurafsky\/slp3\/5.pdf https:\/\/spark-public.s3.amazonaws.com\/cs124\/slides\/ir-1.pdf J+M (3ed) Chapter 6: Vector Semantics, 1-7, 18-26, and review 8-15 &hellip; <\/p>\n<p><a class=\"more-link btn\" href=\"http:\/\/bangla.sitestree.com\/?p=14782\">Continue reading<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[182],"tags":[],"class_list":["post-14782","post","type-post","status-publish","format-standard","hentry","category---blog","item-wrap"],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack-related-posts":[{"id":25013,"url":"http:\/\/bangla.sitestree.com\/?p=25013","url_meta":{"origin":14782,"position":0},"title":"Natural Language Processing Resources #Root","author":"Author-Check- Article-or-Video","date":"April 14, 2021","format":false,"excerpt":"Regular Expressions, Text Normalization, Edit Distance http:\/\/web.stanford.edu\/~jurafsky\/slp3\/2.pdf Language Modeling with N-Grams http:\/\/web.stanford.edu\/~jurafsky\/slp3\/3.pdf Slide: https:\/\/web.stanford.edu\/class\/cs124\/lec\/languagemodeling2019.pdf Chapter 4, \"Naive Bayes and Sentiment Classification\" http:\/\/web.stanford.edu\/~jurafsky\/slp3\/4.pdf Slide: https:\/\/web.stanford.edu\/class\/cs124\/lec\/naivebayes.pdf Thumbs up? Sentiment Classification using Machine Learning Techniques http:\/\/www.cs.cornell.edu\/home\/llee\/papers\/sentiment.pdf Lexicons for Sentiment, Affect, and Connotation http:\/\/web.stanford.edu\/~jurafsky\/slp3\/19.pdf Logistic Regression http:\/\/web.stanford.edu\/~jurafsky\/slp3\/5.pdf https:\/\/spark-public.s3.amazonaws.com\/cs124\/slides\/ir-1.pdf J+M (3ed) Chapter 6: Vector Semantics,\u2026","rel":"","context":"In &quot;FromSitesTree.com&quot;","block_context":{"text":"FromSitesTree.com","link":"http:\/\/bangla.sitestree.com\/?cat=1917"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":26802,"url":"http:\/\/bangla.sitestree.com\/?p=26802","url_meta":{"origin":14782,"position":1},"title":"Bandits and #Reinforcement Learning Course","author":"Sayed","date":"May 2, 2021","format":false,"excerpt":"Best Multi-Armed Bandit Strategy? (feat: UCB Method) https:\/\/www.youtube.com\/watch?v=FgmMK6RPU1c Reinforcement Learning: Complete Course: https:\/\/www.youtube.com\/watch?v=4SLGEq_HZxk&list=PLnn6VZp3hqNvRrdnMOVtgV64F_O-61C1D From the Book by Sutton: http:\/\/incompleteideas.net\/book\/RLbook2018.pdf https:\/\/web.stanford.edu\/class\/psych209\/Readings\/SuttonBartoIPRLBook2ndEd.pdf Just did a google search, and I see that the algorithms from the book are implemented and provided at: https:\/\/github.com\/LyWangPX\/Reinforcement-Learning-2nd-Edition-by-Sutton-Exercise-Solutions Article: https:\/\/towardsdatascience.com\/a-comparison-of-bandit-algorithms-24b4adfcabb https:\/\/towardsdatascience.com\/a-comparison-of-bandit-algorithms-24b4adfcabb","rel":"","context":"In &quot;\u09ac\u09cd\u09b2\u0997 \u0964 Blog&quot;","block_context":{"text":"\u09ac\u09cd\u09b2\u0997 \u0964 Blog","link":"http:\/\/bangla.sitestree.com\/?cat=182"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":16827,"url":"http:\/\/bangla.sitestree.com\/?p=16827","url_meta":{"origin":14782,"position":2},"title":"Misc. Optimization Resources","author":"Sayed","date":"February 9, 2020","format":false,"excerpt":"L0 Norm, L1 Norm, L2 Norm & L-Infinity Norm https:\/\/medium.com\/@montjoile\/l0-norm-l1-norm-l2-norm-l-infinity-norm-7a7d18a4f40c *** Iterative Solutions of Linear Systems https:\/\/www.math.uh.edu\/~jingqiu\/math4364\/iterative_linear_system.pdf *** How statistical Norms improve modeling https:\/\/towardsdatascience.com\/norms-penalties-and-multitask-learning-2f1db5f97c1f Project Example: Optimization: http:\/\/www.cs.cmu.edu\/~aarti\/Class\/10725_Fall17\/past_projects.html https:\/\/web.stanford.edu\/class\/ee392o\/#projects https:\/\/ece.uwaterloo.ca\/~ece602\/Projects\/2017\/Project21\/main.html Area and Project Example: http:\/\/www.ece.tufts.edu\/ee\/194CO\/project_14.pdf Sensor and Optimization: Could be a good read. http:\/\/homepages.rpi.edu\/~mitchj\/phdtheses\/daryn\/ramsdd.pdf ***. ***. *** Note: Older short-notes\u2026","rel":"","context":"In &quot;AI ML DS RL DL NN NLP Data Mining Optimization&quot;","block_context":{"text":"AI ML DS RL DL NN NLP Data Mining Optimization","link":"http:\/\/bangla.sitestree.com\/?cat=1910"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":63958,"url":"http:\/\/bangla.sitestree.com\/?p=63958","url_meta":{"origin":14782,"position":3},"title":"Misc: Optimization: Machine Learning: Data Science Resources","author":"Sayed","date":"June 3, 2021","format":false,"excerpt":"http:\/\/web.mit.edu\/15.053\/www\/AMP-Chapter-09.pdf https:\/\/www.cs.cmu.edu\/~anupamg\/adv-approx\/lecture14.pdf http:\/\/bangla.salearningschool.com\/recent-posts\/misc-optimization\/ https:\/\/www.futurelearn.com\/info\/courses\/maths-linear-quadratic-relations\/0\/steps\/12128 https:\/\/www.mathsisfun.com\/algebra\/systems-linear-equations-matrices.html https:\/\/www.wolframalpha.com\/input\/?i=subspace https:\/\/www.cse.iitk.ac.in\/users\/rmittal\/prev_course\/s14\/notes\/lec3.pdf https:\/\/observablehq.com\/@eliaskal\/point-combinations-linear-conic-affine-convex https:\/\/www.cse.iitk.ac.in\/users\/rmittal\/prev_course\/s14\/course_s14.html http:\/\/bangla.salearningschool.com\/recent-posts\/misc-math-might-relate-to-optimization\/ http:\/\/bangla.salearningschool.com\/recent-posts\/part-x-engineering-optimization-mathematical-optimization\/ https:\/\/www.dr-eriksen.no\/teaching\/GRA6035\/2010\/lecture4.pdf https:\/\/www.mathsisfun.com\/calculus\/concave-up-down-convex.html https:\/\/www-ljk.imag.fr\/membres\/Anatoli.Iouditski\/cours\/convex\/chapitre_3.pdf http:\/\/bangla.salearningschool.com\/recent-posts\/optimization-and-linear-algebra-math-from-the-internet\/ https:\/\/www.thestudentroom.co.uk\/showthread.php?t=1247928 https:\/\/en.wikipedia.org\/wiki\/Hessian_matrix https:\/\/en.wikipedia.org\/wiki\/Newton%27s_method_in_optimization https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/9781118733639.app6 http:\/\/bangla.salearningschool.com\/recent-posts\/optimization-and-linear-algebra-math-from-the-internet\/ https:\/\/en.wikipedia.org\/wiki\/Interior-point_method https:\/\/web.stanford.edu\/~boyd\/papers\/rt_cvx_sig_proc.html Must Read https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/ivantash-optimization_methods_and_their_applications_in_dsp.pdf https:\/\/www.researchgate.net\/publication\/269211254_The_analysis_and_optimization_algorithms_of_the_electronic_circuits_design good one http:\/\/www.bcamath.org\/documentos_public\/courses\/Nogales_2012-13_02_18-22.pdf https:\/\/en.wikipedia.org\/wiki\/Convex_analysis https:\/\/www.khanacademy.org\/search?page_search_query=Optimization%20problems%20(calculus) http:\/\/bangla.salearningschool.com\/recent-posts\/overview-on-optimization-concepts-from-the-internet\/ http:\/\/bangla.salearningschool.com\/recent-posts\/misc-optimization-machine-learning\/ http:\/\/bangla.salearningschool.com\/recent-posts\/misc-math-data-science-machine-learning-optimization-vector-pca-basis-covariance\/ https:\/\/www.mathsisfun.com\/algebra\/matrix-inverse-row-operations-gauss-jordan.html http:\/\/bangla.salearningschool.com\/recent-posts\/misc-math-for-data-science-engineering-and-or-optimization\/","rel":"","context":"In &quot;\u09ac\u09cd\u09b2\u0997 \u0964 Blog&quot;","block_context":{"text":"\u09ac\u09cd\u09b2\u0997 \u0964 Blog","link":"http:\/\/bangla.sitestree.com\/?cat=182"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":16500,"url":"http:\/\/bangla.sitestree.com\/?p=16500","url_meta":{"origin":14782,"position":4},"title":"Overview on optimization concepts: From the Internet","author":"Sayed","date":"December 10, 2019","format":false,"excerpt":"Optimization Concepts: Convex sets: \"A convex set is a set of points such that, given any two points A, B in that set, the line AB joining them lies entirely within that set. Intuitively, this means that the set is connected (so that you can pass between any two points\u2026","rel":"","context":"In &quot;AI ML DS RL DL NN NLP Data Mining Optimization&quot;","block_context":{"text":"AI ML DS RL DL NN NLP Data Mining Optimization","link":"http:\/\/bangla.sitestree.com\/?cat=1910"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":76351,"url":"http:\/\/bangla.sitestree.com\/?p=76351","url_meta":{"origin":14782,"position":5},"title":"lec 6 based on time measures","author":"Sayed","date":"September 15, 2024","format":false,"excerpt":"https:\/\/youtu.be\/7qTcR9w8R7U","rel":"","context":"In &quot;From Youtube Channel&quot;","block_context":{"text":"From Youtube Channel","link":"http:\/\/bangla.sitestree.com\/?cat=1952"},"img":{"alt_text":"","src":"https:\/\/i0.wp.com\/img.youtube.com\/vi\/7qTcR9w8R7U\/0.jpg?resize=350%2C200","width":350,"height":200},"classes":[]}],"_links":{"self":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/posts\/14782","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=14782"}],"version-history":[{"count":0,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=\/wp\/v2\/posts\/14782\/revisions"}],"wp:attachment":[{"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=14782"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=14782"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/bangla.sitestree.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=14782"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}