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  2. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    Major advances in this field can result from advances in learning algorithms (such as deep learning), computer hardware, and, less-intuitively, the availability of high-quality training datasets. [1] High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to ...

  3. Kaggle - Wikipedia

    en.wikipedia.org/wiki/Kaggle

    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.

  4. Anthony Goldbloom - Wikipedia

    en.wikipedia.org/wiki/Anthony_Goldbloom

    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.

  5. Python (programming language) - Wikipedia

    en.wikipedia.org/wiki/Python_(programming_language)

    Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. [33] Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional ...

  6. Information retrieval - Wikipedia

    en.wikipedia.org/wiki/Information_retrieval

    Information retrieval (IR) in computing and information science is the task of identifying and retrieving information system resources that are relevant to an information need.

  7. Deep reinforcement learning - Wikipedia

    en.wikipedia.org/wiki/Deep_reinforcement_learning

    Generally, value-function based methods such as Q-learning are better suited for off-policy learning and have better sample-efficiency - the amount of data required to learn a task is reduced because data is re-used for learning. At the extreme, offline (or "batch") RL considers learning a policy from a fixed dataset without additional ...

  8. Sample entropy - Wikipedia

    en.wikipedia.org/wiki/Sample_entropy

    Like approximate entropy (ApEn), Sample entropy (SampEn) is a measure of complexity. [1] But it does not include self-similar patterns as ApEn does. For a given embedding dimension, tolerance and number of data points, SampEn is the negative natural logarithm of the probability that if two sets of simultaneous data points of length have distance < then two sets of simultaneous data points of ...