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These attributes extend to each of the system's components, including datasets, code, and model parameters, promoting a collaborative and transparent approach to AI development. [1] Free and open-source software (FOSS) licenses, such as the Apache License, MIT License, and GNU General Public License, outline the terms under which open-source ...
NNI (Neural Network Intelligence) is a free and open-source AutoML toolkit developed by Microsoft. [3] [4] It is used to automate feature engineering, model compression, neural architecture search, and hyper-parameter tuning. [5] [6] The source code is licensed under MIT License and available on GitHub. [7]
Deeplearning4j, an open-source, distributed deep learning framework written for the JVM. [81] Keras, a high level open-source software library for machine learning (works on top of other libraries). [82] Microsoft Cognitive Toolkit (previously known as CNTK), an open source toolkit for building artificial neural networks. [83]
The CycL and SubL interpreter (the program that allows users to browse and edit the database as well as to draw inferences) was released free of charge, but only as a binary, without source code. It was made available for Linux and Microsoft Windows. The open source Texai [9] project released the RDF-compatible content extracted from OpenCyc. [10]
Medical open network for AI (MONAI) is an open-source, community-supported framework for Deep learning (DL) in healthcare imaging. MONAI provides a collection of domain-optimized implementations of various DL algorithms and utilities specifically designed for medical imaging tasks.
This is a list of free and open-source software (FOSS) packages, computer software licensed under free software licenses and open-source licenses.Software that fits the Free Software Definition may be more appropriately called free software; the GNU project in particular objects to their works being referred to as open-source. [1]
Code Llama is a fine-tune of LLaMa 2 with code specific datasets. 7B, 13B, and 34B versions were released on August 24, 2023, with the 70B releasing on the January 29, 2024. [29] Starting with the foundation models from LLaMa 2, Meta AI would train an additional 500B tokens of code datasets, before an additional 20B token of long-context data ...
The program uses an XML Schema called AIML (Artificial Intelligence Markup Language) for specifying the heuristic conversation rules. [4] Alice code has been reported to be available as open source. [5] The AIML source is available from ALICE A.I. Foundation on Google Code and from the GitHub account of Richard Wallace.