{"id":43,"date":"2016-02-22T09:52:31","date_gmt":"2016-02-22T08:52:31","guid":{"rendered":"http:\/\/www.pewe.sk\/datalys\/?page_id=43"},"modified":"2019-11-13T14:13:27","modified_gmt":"2019-11-13T13:13:27","slug":"resources","status":"publish","type":"page","link":"https:\/\/www.pewe.sk\/datalys\/resources\/","title":{"rendered":"Resources"},"content":{"rendered":"<p><strong>Keynotes<\/strong><\/p>\n<ul>\n<li><span style=\"color: gray\">[ML]<\/span> <a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2017\/10\/2017-10-05-Datalys-ML-Workflow.pdf\">Machine Learning Workflow<\/a><\/li>\n<li><span style=\"color: gray\">[RecSys]<\/span> <a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2017\/09\/pg_pewe_rec17.pdf\">Introduction to Recommender Systems<\/a><\/li>\n<li><span style=\"color: gray\">[ML]<\/span> <a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2017\/09\/pewe_ml_v1.pdf\">Introduction to Machine Learning<\/a><\/li>\n<li><span style=\"color: gray\">[ML]<\/span> Data Integration (Keynote) <a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2017\/09\/2017-10-12-integracia_dat.zip\">zip<\/a>\u00a0<a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2017\/09\/Integracia_dat.pdf\">pdf<\/a><\/li>\n<li><span style=\"color: gray\">[ML]<\/span> Introduction to Exploratory Analysis: Measures, Visualizations and Practical Examples\u00a0<a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2017\/09\/exploratory_analysis.zip\">zip<\/a>\u00a0<a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2017\/09\/Explorativna_analyza.pdf\">pdf<\/a><\/li>\n<li><span style=\"color: gray\">[REC]<\/span> <a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2019\/05\/Banditi-Datalys-Koncal-a-Zatko.pdf\">Introduction to Multi-Armed Bandits<\/a><\/li>\n<li><span style=\"color: gray\">[ML]<\/span> <a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2019\/04\/pg_how_to_jupyter_3_4_2019.pdf\">Useful tips for working in Jupyter Notebooks<\/a><\/li>\n<li><span style=\"color: gray\">[REC]<\/span> <a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2019\/03\/pg_pewe_rec_in_practice_6_3_2019.pdf\">Recommender Systems in Practice (Part I)<\/a><\/li>\n<li><span style=\"color: gray\">[NLP]<\/span> <a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2018\/11\/Matus-Pikuliak_What-about-deep-NLP.pdf\">What about deep NLP?<\/a><\/li>\n<\/ul>\n<p><strong>Research papers<\/strong><\/p>\n<ul>\n<li><span style=\"color: gray\">[Rec]<\/span> Joseph A. Konstan, John Riedl: <a href=\"https:\/\/link.springer.com\/article\/10.1007\/s11257-011-9112-x\">Recommender systems: from algorithms to user experience<\/a><\/li>\n<li><span style=\"color: gray\">[Rec]<\/span> Qian Zhao, Shuo Chang, F. Maxwell Harper, and Joseph A. Konstan: <a href=\"http:\/\/dl.acm.org\/citation.cfm?id=2959150\">Gaze Prediction for Recommender Systems<\/a> (<a href=\"https:\/\/www.pewe.sk\/wp-content\/uploads\/2016\/01\/reading_gaze_prediction.pptx\">slides<\/a> by Michal Kompan)<\/li>\n<li><span style=\"color: gray\">[Rec]<\/span> Yiming Liu, et al.: <a href=\"http:\/\/dl.acm.org\/citation.cfm?id=2959141\">Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling<\/a> (<a href=\"https:\/\/www.pewe.sk\/wp-content\/uploads\/2016\/01\/pewe_pg_rec_01_12_2016.pdf\">slides<\/a> by Peter Ga\u0161par)<\/li>\n<li><span style=\"color: gray\">[Rec]<\/span> Michael D. Ekstrand, et al.: <a href=\"http:\/\/dl.acm.org\/citation.cfm?id=2800195\">Letting Users Choose Recommender Algorithms: An Experimental Study<\/a> (<a href=\"https:\/\/www.pewe.sk\/wp-content\/uploads\/2016\/01\/pewe_pg_rec_06_10_2016.pdf\">slides<\/a> by Peter Ga\u0161par)<\/li>\n<li><span style=\"color: gray\">[ML]<\/span> Pedro Domingos: <a href=\"http:\/\/homes.cs.washington.edu\/~pedrod\/papers\/cacm12.pdf\">A Few Useful Things to Know about Machine Learning<\/a> (<a href=\"https:\/\/www.pewe.sk\/wp-content\/uploads\/2016\/01\/presentation-A-Few-Useful-Things-to-Know-about-Machine-Learning.pptx\">slides<\/a> by Ondrej Ka\u0161\u0161\u00e1k)<\/li>\n<li><span style=\"color: gray\">[NLP]<\/span> Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean: <a href=\"https:\/\/arxiv.org\/abs\/1301.3781\">Efficient Estimation of Word Representations in Vector Space<\/a><\/li>\n<\/ul>\n<p><strong>Books<\/strong><\/p>\n<ul>\n<li><span style=\"color: gray\">[Rec]<\/span> <a href=\"http:\/\/www.springer.com\/us\/book\/9783319296579\">Recommender Systems &#8211; The Textbook<\/a><\/li>\n<li><span style=\"color: gray\">[Rec]<\/span> <a href=\"http:\/\/www.springer.com\/la\/book\/9781489976369\">Recommender Systems Handbook (2nd Edition)<\/a><\/li>\n<li><span style=\"color: gray\">[ML]<\/span> <a href=\"https:\/\/www.elsevier.com\/books\/introduction-to-statistical-machine-learning\/sugiyama\/978-0-12-802121-7\">Introduction to Statistical Machine Learning<\/a><\/li>\n<li><span style=\"color: gray\">[ML]<\/span> <a href=\"http:\/\/www.springer.com\/gb\/book\/9781461471370\">An Introduction to Statistical Learning with Applications in R<\/a><\/li>\n<\/ul>\n<p><strong>Books (available in printed form)<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/www.amazon.com\/Information-Visualization-Introduction-Robert-Spence-dp-3319073400\/dp\/3319073400\/ref=mt_paperback?_encoding=UTF8&amp;me=&amp;qid=1560951999\">Information Visualization: An Introduction<\/a><\/li>\n<li><a href=\"https:\/\/www.amazon.com\/Information-Visualization-Perception-Interactive-Technologies-dp-0123814642\/dp\/0123814642\/ref=mt_hardcover?_encoding=UTF8&amp;me=&amp;qid=1560951999\">Information Visualization: Perception for Design (Interactive Technologies)<\/a><\/li>\n<li><a href=\"https:\/\/www.amazon.com\/Bayesian-Statistics-Fun-Will-Kurt\/dp\/1593279566\/\"><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Bayesian Statistics the Fun Way&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:513,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0}\">Bayesian Statistics the Fun Way<\/span><\/a><\/li>\n<li><a href=\"https:\/\/www.amazon.com\/Craft-Scientific-Presentations-Critical-Succeed\/dp\/1441982787\/ref=sr_1_1?keywords=the+craft+of+scientific+presentations&amp;qid=1561990363&amp;s=gateway&amp;sr=8-1\"><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;The Craft of Scientific Presentations&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:513,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0}\">The Craft of Scientific Presentations<\/span><\/a><\/li>\n<li><a href=\"https:\/\/www.amazon.com\/Craft-Scientific-Writing-Michael-Alley\/dp\/1441982876\/ref=sr_1_1?crid=9OFJ4BS8CJ2Z&amp;keywords=the+craft+of+scientific+writing&amp;qid=1561990413&amp;s=gateway&amp;sprefix=the+craft+of+scientific+writin%2Caps%2C222&amp;sr=8-1\"><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;The Craft of Scientific Writing&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:513,&quot;3&quot;:{&quot;1&quot;:0},&quot;12&quot;:0}\">The Craft of Scientific Writing<\/span><\/a><\/li>\n<\/ul>\n<p><strong>Blogs<\/strong><\/p>\n<ul>\n<li><span style=\"color: gray\">[AI]<\/span> <a href=\"https:\/\/towardsai.net\/\">Towards AI<\/a><\/li>\n<li><span style=\"color: gray\">[Data Science]<\/span> <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a><\/li>\n<li><span style=\"color: gray\">[Data Science]<\/span> <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/\">Analytics Vidhya<\/a><\/li>\n<li><span style=\"color: gray\">[Data Science]<\/span> <a href=\"https:\/\/datascience.berkeley.edu\/blog\/\">Data Science @ Berkeley<\/a><\/li>\n<li><span style=\"color: gray\">[Data Science]<\/span> <a href=\"https:\/\/www.analyticsvidhya.com\/blog\/2016\/01\/guide-data-exploration\/\">A Comprehensive Guide to Data Exploration<\/a><\/li>\n<li><span style=\"color: gray\">[Computer Science]<\/span> <a href=\"https:\/\/www.youtube.com\/watch?v=SzJ46YA_RaA\">Map of Computer Science<\/a> (<a href=\"https:\/\/i.redd.it\/tay35pngbnkz.png\">Static map to download<\/a>)<\/li>\n<li><span style=\"color: gray\">[Data Science]<\/span> <a href=\"https:\/\/machinelearningmastery.com\/\">Machine learning mastery<\/a><\/li>\n<li><span style=\"color: gray\">[Data Science]<\/span> <a href=\"https:\/\/www.kdnuggets.com\/\">KDnuggets<\/a><\/li>\n<\/ul>\n<p><strong>Courses<\/strong><\/p>\n<ul>\n<li><span style=\"color: gray\">[ML]<\/span> <a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2017\/09\/2017-09-21_machine_learning_coursera.pdf\">Machine learning at Coursera<\/a> (<a href=\"https:\/\/www.pewe.sk\/datalys\/2016\/12\/27\/machine-learning-at-cousera\/\">blog<\/a>)<\/li>\n<li><span style=\"color: gray\">[ML]<\/span> <a href=\"https:\/\/www.edx.org\/course\/introduction-python-data-science-microsoft-dat208x-7\">Python For Data Science at edX<\/a> (<a href=\"https:\/\/www.pewe.sk\/datalys\/2017\/01\/19\/introduction-to-python-for-data-science-at-edx\/\">blog<\/a>)<\/li>\n<li><span style=\"color: gray\">[NLP]<\/span> <a href=\"https:\/\/www.coursera.org\/learn\/natural-language-processing\">Introduction to NLP at Coursera<\/a> (<a href=\"https:\/\/www.pewe.sk\/datalys\/2016\/12\/11\/introduction-to-nlp-at-coursera\/\">blog<\/a>)<\/li>\n<\/ul>\n<p><strong>How to Start with Python<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/docs.python.org\/3\/tutorial\/index.html\">Python basics<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/rajathkumarmp\/Python-Lectures\">Practical introduction to Python<\/a><\/li>\n<li><a href=\"http:\/\/pandas.pydata.org\/pandas-docs\/stable\/10min.html\">Introduction to Pandas<\/a><\/li>\n<li><a href=\"http:\/\/nbviewer.jupyter.org\/github\/rasbt\/python_reference\/blob\/master\/tutorials\/things_in_pandas.ipynb\">Practical examples in Pandas<\/a><\/li>\n<li><a href=\"http:\/\/www.analyticsvidhya.com\/blog\/2016\/01\/complete-tutorial-learn-data-science-python-scratch-2\/\">Introduction to Data Science<\/a><\/li>\n<li><a href=\"http:\/\/www.scipy-lectures.org\/\">Another complex tutorial for Data Science<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/JWarmenhoven\/ISLR-python\">An Introduction to Statistical Learning<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/nborwankar\/LearnDataScience\">Machine learning in Python<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/yhat\/DataGotham2013\/\">Useful Python notebooks<\/a><\/li>\n<li><a href=\"https:\/\/github.com\/jupyter\/jupyter\/wiki\/A-gallery-of-interesting-Jupyter-and-IPython-Notebooks\">Another gallery of interesting Jupyter and IPython Notebooks<\/a><\/li>\n<li><a href=\"https:\/\/pythontips.com\/2016\/02\/27\/learning-python-for-data-science\/\">More resources<\/a><\/li>\n<\/ul>\n<p><strong>Datasets<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/toolbox.google.com\/datasetsearch\">Google Dataset Search<\/a><\/li>\n<li><a href=\"http:\/\/academictorrents.com\/browse.php?cat=6\">Academic Torents<\/a><\/li>\n<li><a href=\"https:\/\/www.yelp.com\/dataset\/challenge\">Yelp<\/a><\/li>\n<li><span style=\"color: gray\">[REC]<\/span> <a href=\"https:\/\/grouplens.org\/datasets\/movielens\/\">MovieLens<\/a><\/li>\n<\/ul>\n<p><strong>Technologies<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2016\/02\/2017-09-21_bayesian_block.pdf\">Bayesian blocks: Histogram bin size selection<\/a><\/li>\n<\/ul>\n<p><strong>AI Research Residency Programs<\/strong> (<a href=\"https:\/\/twitter.com\/hardmaru\/status\/966147857328697344\/photo\/1\">source<\/a>)<\/p>\n<ul>\n<li><a href=\"https:\/\/research.google.com\/teams\/brain\/residency\/\">Google AI Residency Program<\/a><\/li>\n<li><a href=\"https:\/\/research.fb.com\/programs\/facebook-ai-research-residency-program\/\">Facebook AI Residency Program<\/a><\/li>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/academic-program\/microsoft-ai-residency-program\/\">Microsoft AI Residency Program<\/a><\/li>\n<li><a href=\"http:\/\/microsoftnewengland.com\/nerdAI\/\">Microsoft NERD Artificial Intelligence Program<\/a><\/li>\n<li><a href=\"https:\/\/eng.uber.com\/uber-ai-residency\/\">Uber AI Residency Program<\/a><\/li>\n<\/ul>\n<p><strong>Computation resources<\/strong><\/p>\n<ul>\n<li>Data centre &#8211; 736 cores, 10TB ram, 100TB disk<\/li>\n<li>Smart &#8211; 16 nodes, 128 cores, 0.6TB ram, 48TB disk<\/li>\n<\/ul>\n<p><strong>Misc<\/strong><\/p>\n<ul>\n<li><a href=\"https:\/\/www.pewe.sk\/datalys\/wp-content\/uploads\/sites\/3\/2017\/09\/Datalys_Presentation-template_v1.pptx\">Datalys Presentation Template<\/a> (last update: 19th September 2017)<\/li>\n<li><a href=\"https:\/\/docs.google.com\/spreadsheets\/d\/1rs2EXUhhxflgJmxRR-a1Bjj6y9XVorTHjjCVTSxpaOQ\/edit#gid=0\">Datalys Slovak-English Domain Term Dictionary<\/a><\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Keynotes [ML] Machine Learning Workflow [RecSys] Introduction to Recommender Systems [ML] Introduction to Machine Learning [ML] Data Integration (Keynote) zip\u00a0pdf [ML] Introduction to [&hellip;]<\/p>\n","protected":false},"author":13,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/www.pewe.sk\/datalys\/wp-json\/wp\/v2\/pages\/43"}],"collection":[{"href":"https:\/\/www.pewe.sk\/datalys\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.pewe.sk\/datalys\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.pewe.sk\/datalys\/wp-json\/wp\/v2\/users\/13"}],"replies":[{"embeddable":true,"href":"https:\/\/www.pewe.sk\/datalys\/wp-json\/wp\/v2\/comments?post=43"}],"version-history":[{"count":43,"href":"https:\/\/www.pewe.sk\/datalys\/wp-json\/wp\/v2\/pages\/43\/revisions"}],"predecessor-version":[{"id":987,"href":"https:\/\/www.pewe.sk\/datalys\/wp-json\/wp\/v2\/pages\/43\/revisions\/987"}],"wp:attachment":[{"href":"https:\/\/www.pewe.sk\/datalys\/wp-json\/wp\/v2\/media?parent=43"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}