<?xml version="1.0" encoding="UTF-8"?>
<rdf:RDF xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"><channel rdf:about="https://www.bibsonomy.org/concept/user/hotho/web2.0mining"><title>BibSonomy bookmarks for /concept/user/hotho/web2.0mining</title><link>https://www.bibsonomy.org/concept/user/hotho/web2.0mining</link><description>BibSonomy RSS Feed for /concept/user/hotho/web2.0mining</description><items><rdf:Seq><rdf:li rdf:resource="https://predictiveprivacy.org/"/><rdf:li rdf:resource="https://pythonawesome.com/must-read-papers-on-pre-trained-language-models/"/><rdf:li rdf:resource="http://phontron.com/class/nn4nlp2019/"/><rdf:li rdf:resource="https://www.plattform-lernende-systeme.de/files/Downloads/Publikationen/AG1_Whitepaper_280619.pdf"/><rdf:li rdf:resource="https://www.notion.so/Kara-Primer-1b5290d3205e448384c6a365d2444f61"/><rdf:li rdf:resource="https://www.slideshare.net/fabien_gandon/wimmics-research-team-overview-2017"/><rdf:li rdf:resource="https://dkm-static.fbk.eu/resources/phdthesis/GiulioPetrucci_PhD-Thesis.pdf"/><rdf:li rdf:resource="http://www.guide2research.com/topconf/machine-learning"/><rdf:li rdf:resource="http://matpalm.com/blog/counting_bees/"/><rdf:li rdf:resource="http://cuda-z.sourceforge.net"/><rdf:li rdf:resource="https://github.com/oxford-cs-deepnlp-2017/lectures"/><rdf:li rdf:resource="http://web.stanford.edu/class/cs224n/syllabus.html"/><rdf:li rdf:resource="https://github.com/edobashira/speech-language-processing"/><rdf:li rdf:resource="https://github.com/SeanNaren/deepspeech.pytorch"/><rdf:li rdf:resource="https://github.com/rasbt/python-machine-learning-book-2nd-edition#whats-new-in-the-second-edition-from-the-first-edition"/><rdf:li rdf:resource="https://scholar.google.com/citations?hl=en&amp;view_op=top_venues&amp;vq=eng_artificialintelligence"/><rdf:li rdf:resource="https://www.quora.com/What-are-some-must-read-papers-on-machine-learning-for-a-beginner"/><rdf:li rdf:resource="https://conductrics.com/data-science-resources/"/><rdf:li rdf:resource="http://www.spiegel.de/netzwelt/web/pew-studie-social-media-nutzung-in-deutschland-im-weltweiten-vergleich-niedrig-a-1078787.html"/><rdf:li rdf:resource="http://www.henningschuerig.de/2010/social-media-statt-web-20/"/></rdf:Seq></items></channel><item rdf:about="https://predictiveprivacy.org/"><title>The Predictive Privacy Project - Rainer Mühlhoff</title><description>Data Protection in the Context of Big Data and AI</description><link>https://predictiveprivacy.org/</link><dc:creator>hotho</dc:creator><dc:date>2022-10-13T10:56:55+02:00</dc:date><dc:subject>Datenschutz big data deep learning machine ml predictive privacy </dc:subject><content:encoded>&lt;span itemprop=&#034;description&#034;&gt;Data Protection in the Context of Big Data and AI&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Datenschutz"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/big"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/data"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/deep"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/machine"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/predictive"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/privacy"/></rdf:Bag></taxo:topics></item><item rdf:about="https://pythonawesome.com/must-read-papers-on-pre-trained-language-models/"><title>Must-read Papers on pre-trained language models</title><description></description><link>https://pythonawesome.com/must-read-papers-on-pre-trained-language-models/</link><dc:creator>hotho</dc:creator><dc:date>2020-09-12T11:17:07+02:00</dc:date><dc:subject>bert elmo language model deep learning ml pre-trained </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2020-09-12T11:17:07+02:00&#034; href=&#034;https://pythonawesome.com/must-read-papers-on-pre-trained-language-models/&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://pythonawesome.com/must-read-papers-on-pre-trained-language-models/&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/bert"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/elmo"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/language"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/model"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/deep"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/pre-trained"/></rdf:Bag></taxo:topics></item><item rdf:about="http://phontron.com/class/nn4nlp2019/"><title>CS 11-747: Neural Networks for NLP</title><description></description><link>http://phontron.com/class/nn4nlp2019/</link><dc:creator>hotho</dc:creator><dc:date>2020-06-02T08:57:52+02:00</dc:date><dc:subject>deep language learning ml nlp </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2020-06-02T08:57:52+02:00&#034; href=&#034;http://phontron.com/class/nn4nlp2019/&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;http://phontron.com/class/nn4nlp2019/&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/deep"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/language"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/nlp"/></rdf:Bag></taxo:topics></item><item rdf:about="https://www.plattform-lernende-systeme.de/files/Downloads/Publikationen/AG1_Whitepaper_280619.pdf"><title>KI made in Germany</title><description></description><link>https://www.plattform-lernende-systeme.de/files/Downloads/Publikationen/AG1_Whitepaper_280619.pdf</link><dc:creator>hotho</dc:creator><dc:date>2019-10-03T21:12:41+02:00</dc:date><dc:subject>Germany ai deep ki learning machine ml paper white </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2019-10-03T21:12:41+02:00&#034; href=&#034;https://www.plattform-lernende-systeme.de/files/Downloads/Publikationen/AG1_Whitepaper_280619.pdf&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://www.plattform-lernende-systeme.de/files/Downloads/Publikationen/AG1_Whitepaper_280619.pdf&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Germany"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ai"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/deep"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ki"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/machine"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/paper"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/white"/></rdf:Bag></taxo:topics></item><item rdf:about="https://www.notion.so/Kara-Primer-1b5290d3205e448384c6a365d2444f61"><title>Kara Primer</title><description>A new tool that blends your everyday work apps into one. It&#039;s the all-in-one workspace for you and your team</description><link>https://www.notion.so/Kara-Primer-1b5290d3205e448384c6a365d2444f61</link><dc:creator>hotho</dc:creator><dc:date>2019-08-12T08:30:27+02:00</dc:date><dc:subject>Datenschutz Medizin analytics data kara ml primer privacy </dc:subject><content:encoded>&lt;span itemprop=&#034;description&#034;&gt;A new tool that blends your everyday work apps into one. It&amp;#039;s the all-in-one workspace for you and your team&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Datenschutz"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Medizin"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/analytics"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/data"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/kara"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/primer"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/privacy"/></rdf:Bag></taxo:topics></item><item rdf:about="https://www.slideshare.net/fabien_gandon/wimmics-research-team-overview-2017"><title>Wimmics Research Team Overview 2017</title><description>Overview of the research domain and works of the joint research team Wimmics (UCS, Inria, CNRS, I3S) in Sophia Antipolis, France</description><link>https://www.slideshare.net/fabien_gandon/wimmics-research-team-overview-2017</link><dc:creator>hotho</dc:creator><dc:date>2018-12-04T11:19:06+01:00</dc:date><dc:subject>***** fabien gandon overview semantic slides social web </dc:subject><content:encoded>&lt;span itemprop=&#034;description&#034;&gt;Overview of the research domain and works of the joint research team Wimmics (UCS, Inria, CNRS, I3S) in Sophia Antipolis, France&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/*****"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/fabien"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/gandon"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/overview"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/semantic"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/slides"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/social"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/web"/></rdf:Bag></taxo:topics></item><item rdf:about="https://dkm-static.fbk.eu/resources/phdthesis/GiulioPetrucci_PhD-Thesis.pdf"><title>Learning to Learn Concept Descriptions</title><description></description><link>https://dkm-static.fbk.eu/resources/phdthesis/GiulioPetrucci_PhD-Thesis.pdf</link><dc:creator>hotho</dc:creator><dc:date>2018-10-08T23:53:14+02:00</dc:date><dc:subject>Concept Descriptions Learn Learning deep ml ol toread </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2018-10-08T23:53:14+02:00&#034; href=&#034;https://dkm-static.fbk.eu/resources/phdthesis/GiulioPetrucci_PhD-Thesis.pdf&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://dkm-static.fbk.eu/resources/phdthesis/GiulioPetrucci_PhD-Thesis.pdf&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Concept"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Descriptions"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Learn"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/deep"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ol"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/toread"/></rdf:Bag></taxo:topics></item><item rdf:about="http://www.guide2research.com/topconf/machine-learning"><title>Top Conferences for Machine Learning &amp; Arti. Intelligence</title><description></description><link>http://www.guide2research.com/topconf/machine-learning</link><dc:creator>hotho</dc:creator><dc:date>2018-08-30T08:34:46+02:00</dc:date><dc:subject>ai conference dm ki ml top </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2018-08-30T08:34:46+02:00&#034; href=&#034;http://www.guide2research.com/topconf/machine-learning&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;http://www.guide2research.com/topconf/machine-learning&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ai"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/conference"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/dm"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ki"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/top"/></rdf:Bag></taxo:topics></item><item rdf:about="http://matpalm.com/blog/counting_bees/"><title>brain of mat kelcey</title><description></description><link>http://matpalm.com/blog/counting_bees/</link><dc:creator>hotho</dc:creator><dc:date>2018-06-05T15:04:09+02:00</dc:date><dc:subject>bee counting image ml </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2018-06-05T15:04:09+02:00&#034; href=&#034;http://matpalm.com/blog/counting_bees/&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;http://matpalm.com/blog/counting_bees/&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/bee"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/counting"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/image"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/></rdf:Bag></taxo:topics></item><item rdf:about="http://cuda-z.sourceforge.net"><title>CUDA-Z</title><description></description><link>http://cuda-z.sourceforge.net</link><dc:creator>hotho</dc:creator><dc:date>2018-05-19T12:06:49+02:00</dc:date><dc:subject>Nvidia cuda mac ml test </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2018-05-19T12:06:49+02:00&#034; href=&#034;http://cuda-z.sourceforge.net&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;http://cuda-z.sourceforge.net&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Nvidia"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/cuda"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/mac"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/test"/></rdf:Bag></taxo:topics></item><item rdf:about="https://github.com/oxford-cs-deepnlp-2017/lectures"><title>oxford-cs-deepnlp-2017/lectures: Oxford Deep NLP 2017 course</title><description></description><link>https://github.com/oxford-cs-deepnlp-2017/lectures</link><dc:creator>hotho</dc:creator><dc:date>2017-10-27T15:09:54+02:00</dc:date><dc:subject>course deep learning lecture ml nlp </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2017-10-27T15:09:54+02:00&#034; href=&#034;https://github.com/oxford-cs-deepnlp-2017/lectures&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://github.com/oxford-cs-deepnlp-2017/lectures&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/course"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/deep"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/lecture"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/nlp"/></rdf:Bag></taxo:topics></item><item rdf:about="http://web.stanford.edu/class/cs224n/syllabus.html"><title>CS224n: Natural Language Processing with Deep Learning</title><description></description><link>http://web.stanford.edu/class/cs224n/syllabus.html</link><dc:creator>hotho</dc:creator><dc:date>2017-10-27T15:09:14+02:00</dc:date><dc:subject>deep language learning lecture lehre ml natural nlp processing </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2017-10-27T15:09:14+02:00&#034; href=&#034;http://web.stanford.edu/class/cs224n/syllabus.html&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;http://web.stanford.edu/class/cs224n/syllabus.html&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/deep"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/language"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/lecture"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/lehre"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/natural"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/nlp"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/processing"/></rdf:Bag></taxo:topics></item><item rdf:about="https://github.com/edobashira/speech-language-processing"><title>edobashira/speech-language-processing: A curated list of speech and natural language processing resources</title><description>speech-language-processing - A curated list of speech and natural language processing resources</description><link>https://github.com/edobashira/speech-language-processing</link><dc:creator>hotho</dc:creator><dc:date>2017-10-13T08:56:38+02:00</dc:date><dc:subject>language ml model nlp overview recognition speech tools </dc:subject><content:encoded>&lt;span itemprop=&#034;description&#034;&gt;speech-language-processing - A curated list of speech and natural language processing resources&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/language"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/model"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/nlp"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/overview"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/recognition"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/speech"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/tools"/></rdf:Bag></taxo:topics></item><item rdf:about="https://github.com/SeanNaren/deepspeech.pytorch"><title>SeanNaren/deepspeech.pytorch: Speech Recognition using DeepSpeech2 and the CTC activation function. Edit</title><description>deepspeech.pytorch - Speech Recognition using DeepSpeech2 and the CTC activation function. Edit</description><link>https://github.com/SeanNaren/deepspeech.pytorch</link><dc:creator>hotho</dc:creator><dc:date>2017-10-13T08:53:29+02:00</dc:date><dc:subject>deep learning ml recognition speech text </dc:subject><content:encoded>&lt;span itemprop=&#034;description&#034;&gt;deepspeech.pytorch - Speech Recognition using DeepSpeech2 and the CTC activation function. Edit&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/deep"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/recognition"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/speech"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/text"/></rdf:Bag></taxo:topics></item><item rdf:about="https://github.com/rasbt/python-machine-learning-book-2nd-edition#whats-new-in-the-second-edition-from-the-first-edition"><title>rasbt/python-machine-learning-book-2nd-edition: The &#034;Python Machine Learning (2nd edition)&#034; book code repository and info resource</title><description>python-machine-learning-book-2nd-edition - The &#034;Python Machine Learning (2nd edition)&#034; book code repository and info resource</description><link>https://github.com/rasbt/python-machine-learning-book-2nd-edition#whats-new-in-the-second-edition-from-the-first-edition</link><dc:creator>hotho</dc:creator><dc:date>2017-09-23T21:21:01+02:00</dc:date><dc:subject>book learning machine ml python reading toread </dc:subject><content:encoded>&lt;span itemprop=&#034;description&#034;&gt;python-machine-learning-book-2nd-edition - The &amp;#034;Python Machine Learning (2nd edition)&amp;#034; book code repository and info resource&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/book"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/machine"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/python"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/reading"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/toread"/></rdf:Bag></taxo:topics></item><item rdf:about="https://scholar.google.com/citations?hl=en&amp;view_op=top_venues&amp;vq=eng_artificialintelligence"><title>Artificial Intelligence - Google Scholar Metrics</title><description></description><link>https://scholar.google.com/citations?hl=en&amp;view_op=top_venues&amp;vq=eng_artificialintelligence</link><dc:creator>hotho</dc:creator><dc:date>2017-09-23T21:16:55+02:00</dc:date><dc:subject>ai learning machine ml reading </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2017-09-23T21:16:55+02:00&#034; href=&#034;https://scholar.google.com/citations?hl=en&amp;amp;view_op=top_venues&amp;amp;vq=eng_artificialintelligence&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://scholar.google.com/citations?hl=en&amp;amp;view_op=top_venues&amp;amp;vq=eng_artificialintelligence&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ai"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/machine"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/reading"/></rdf:Bag></taxo:topics></item><item rdf:about="https://www.quora.com/What-are-some-must-read-papers-on-machine-learning-for-a-beginner"><title>What are some must read papers on machine learning for a beginner? - Quora</title><description></description><link>https://www.quora.com/What-are-some-must-read-papers-on-machine-learning-for-a-beginner</link><dc:creator>hotho</dc:creator><dc:date>2017-09-23T21:16:18+02:00</dc:date><dc:subject>learning machine ml reading </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2017-09-23T21:16:18+02:00&#034; href=&#034;https://www.quora.com/What-are-some-must-read-papers-on-machine-learning-for-a-beginner&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://www.quora.com/What-are-some-must-read-papers-on-machine-learning-for-a-beginner&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/machine"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/reading"/></rdf:Bag></taxo:topics></item><item rdf:about="https://conductrics.com/data-science-resources/"><title>A List of Data Science and Machine Learning Resources - Conductrics</title><description></description><link>https://conductrics.com/data-science-resources/</link><dc:creator>hotho</dc:creator><dc:date>2017-09-23T20:21:54+02:00</dc:date><dc:subject>learning list machine ml reading </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2017-09-23T20:21:54+02:00&#034; href=&#034;https://conductrics.com/data-science-resources/&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;https://conductrics.com/data-science-resources/&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/learning"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/list"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/machine"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/ml"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/reading"/></rdf:Bag></taxo:topics></item><item rdf:about="http://www.spiegel.de/netzwelt/web/pew-studie-social-media-nutzung-in-deutschland-im-weltweiten-vergleich-niedrig-a-1078787.html"><title>Pew-Studie: Social-Media-Nutzung in Deutschland im weltweiten Vergleich niedrig - SPIEGEL ONLINE</title><description>85 Prozent der Deutschen sind einer US-Studie zufolge im Internet aktiv. Die Begeisterung für soziale Medien ist hierzulande trotzdem deutlich geringer als in vielen anderen Ländern.</description><link>http://www.spiegel.de/netzwelt/web/pew-studie-social-media-nutzung-in-deutschland-im-weltweiten-vergleich-niedrig-a-1078787.html</link><dc:creator>hotho</dc:creator><dc:date>2017-06-05T20:51:33+02:00</dc:date><dc:subject>Nutzung media social soziale verhalten </dc:subject><content:encoded>&lt;span itemprop=&#034;description&#034;&gt;85 Prozent der Deutschen sind einer US-Studie zufolge im Internet aktiv. Die Begeisterung für soziale Medien ist hierzulande trotzdem deutlich geringer als in vielen anderen Ländern.&lt;/span&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/Nutzung"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/media"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/social"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/soziale"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/verhalten"/></rdf:Bag></taxo:topics></item><item rdf:about="http://www.henningschuerig.de/2010/social-media-statt-web-20/"><title>Social Media statt Web 2.0 | Henning Schürig</title><description></description><link>http://www.henningschuerig.de/2010/social-media-statt-web-20/</link><dc:creator>hotho</dc:creator><dc:date>2017-04-15T17:11:41+02:00</dc:date><dc:subject>2.0 social social_web web wj2017 </dc:subject><content:encoded>&lt;a itemprop=&#034;url&#034; data-versiondate=&#034;2017-04-15T17:11:41+02:00&#034; href=&#034;http://www.henningschuerig.de/2010/social-media-statt-web-20/&#034; rel=&#034;nofollow&#034; class=&#034;description-link&#034;&gt;http://www.henningschuerig.de/2010/social-media-statt-web-20/&lt;/a&gt;</content:encoded><taxo:topics><rdf:Bag><rdf:li rdf:resource="https://www.bibsonomy.org/tag/2.0"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/social"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/social_web"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/web"/><rdf:li rdf:resource="https://www.bibsonomy.org/tag/wj2017"/></rdf:Bag></taxo:topics></item></rdf:RDF>