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Machine learning (ml) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalise to unseen data, and thus perform tasks without explicit instructions It is closely related to regression and classification, but the goal is to learn a similarity function that measures how similar or related two objects are. [1] within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms.

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[503] web platform with python, r, java, and other apis for downloading hundreds of machine learning datasets, evaluating algorithms on datasets, and benchmarking algorithm performance against dozens of other algorithms Similarity learning is an area of supervised machine learning in artificial intelligence [504] a large, curated repository of benchmark datasets for evaluating supervised machine learning algorithms.

Chainer is an open source deep learning framework written purely in python on top of numpy and cupy python libraries

The development is led by japanese venture company preferred networks in partnership with ibm, intel, microsoft, and nvidia It is developed by google with contributions from nvidia and other community contributors [1][2][3] it is described as bringing together a modified version of autograd (automatic obtaining of the gradient function. [4][5] it is based on decision tree algorithms and used for ranking, classification and other machine learning tasks

The development focus is on performance and scalability. It is the combination of automation and ml.