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Kaggle is a data science competition platform and online community for data scientists and machine learning practitioners under Google LLC.Kaggle enables users to find and publish datasets, explore and build models in a web-based data science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.
Research has been completed on how competition can improve research performance. Companies like JPMorgan Chase also run internal contests involving large numbers of employees. [2] Examples of data science competition platforms include Bitgrit, [3] Correlation One, Kaggle, InnoCentive, Microprediction, [4] AIcrowd, [5] and Alibaba Tianchi. [6]
Anthony John Goldbloom (born 21 June 1983) is the founder and former CEO of Kaggle, a data science competition platform which has used predictive modelling competitions to solve data problems for companies, such as NASA, Wikipedia, [1] Ford and Deloitte.
Annual competition organized and sponsored by Google from 2003 until its cancellation in 2023. [9] 32,702 (2022) [10] International Collegiate Programming Contest (ICPC) [11] ICPC Foundation university students Team competition for university students, the contest consists of many regional rounds that conclude in a world final organized yearly.
Free Music Archive: Audio under Creative Commons from 100k songs (343 days, 1TiB) with a hierarchy of 161 genres, metadata, user data, free-form text. Raw audio and audio features. 106,574 Text, MP3 Classification, recommendation 2017 [143] M. Defferrard et al. Bach Choral Harmony Dataset Bach chorale chords. Audio features extracted. 5665 Text
ACSL, or the American Computer Science League, is an international computer science competition among more than 300 schools. [1] Originally founded in 1978 as the Rhode Island Computer Science League, it then became the New England Computer Science League. With countrywide and worldwide participants, it became the American Computer Science League.
Data science is an interdisciplinary field [10] focused on extracting knowledge from typically large data sets and applying the knowledge from that data to solve problems in other application domains.
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.