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Comparison of numerical-analysis software; Comparison of statistical packages; Comparison of cognitive architectures; List of datasets for machine-learning research; List of numerical-analysis software
Key topics include machine learning, deep learning, natural language processing and computer vision. Many universities now offer specialized programs in AI engineering at both the undergraduate and postgraduate levels, including hands-on labs, project-based learning, and interdisciplinary courses that bridge AI theory with engineering practices ...
Microsoft Cognitive Toolkit, [3] previously known as CNTK and sometimes styled as The Microsoft Cognitive Toolkit, is a deprecated [4] deep learning framework developed by Microsoft Research. Microsoft Cognitive Toolkit describes neural networks as a series of computational steps via a directed graph .
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning. The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
In computer science, a bridging model is an abstract model of a computer which provides a conceptual bridge between the physical implementation of the machine and the abstraction available to a programmer of that machine; in other words, it is intended to provide a common level of understanding between hardware and software engineers.
For example, Bengio and LeCun (2007) wrote an article regarding local vs non-local learning, as well as shallow vs deep architecture. [ 231 ] Biological brains use both shallow and deep circuits as reported by brain anatomy, [ 232 ] displaying a wide variety of invariance.
Software testing can also be performed by non-dedicated software testers. In the 1980s, the term software tester started to be used to denote a separate profession. Notable software testing roles and titles include: [65] test manager, test lead, test analyst, test designer, tester, automation developer, and test administrator. [66]
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]