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AIMeetup #2: Gdzie można nakarmić sztuczną inteligencję?
The document discusses various methods for providing data to feed machine learning algorithms, emphasizing the quantity of labeled examples needed for acceptable performance. It suggests options like paying individuals for data, utilizing crowdsourcing platforms like Mechanical Turk, and purchasing existing datasets. Additionally, it mentions sources for free datasets and provides links for further exploration.
AIMeetup #2: Gdzie można nakarmić sztuczną inteligencję?
- 1. Gdzie można nakarmić sztucznąinteligencję? dr inż. Aleksander Smywiński-Pohl Akademia Górniczo-Hutnicza, Techmo apohllo.pl https://goo.gl/Im3 bk7
- 2. Algorytmy uczenia maszynowegosą głodne deeplearningbook.org: “As of 2016, a rough rule of thumb is that a supervised deep learning algorithm will generally achieve acceptable performance with around 5,000 labeled examples per category, and will match or exceed human performance when trained with a dataset containing at least 10 million labeled examples. https://goo.gl/Eu 2vYq
- 3. Zapłać ludziom, żebynakarmili twoje algorytmy... Mechanical Turk is a marketplace for work https://goo.gl/n1 girM
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- 5. ...zamień użytkowników wdarmowych pracowników https://goo.gl/PGFAvO “The system has been reported as displaying over 100 million CAPTCHAs every day” Wikipedia
- 6. Kup gotowe zbiorydanych https://www.ldc.upenn.edu/ LDC93S1 TIMIT Acoustic-Phonetic Continuous Speech Corpus DC2006T13 Web 1T 5-gram Version 1 LDC96L14 CELEX2 LDC2013T19 OntoNotes Release 5.0 LDC93S10 TIDIGITS LDC99T42 Treebank-3 LDC2008T19 The New York Times Annotated Corpus LDC93T3A TIPSTER Complete LDC97S62 Switchboard-1 Release 2 LDC2016T19 BOLT Chinese-English Word Alignment and Tagging
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- 8. Wykorzystaj “darmowe” zbiorydanych https://goo.gl/3Tk WSJ
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