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2. Open-source artificial intelligence - Wikipedia

   en.wikipedia.org/wiki/Open-source_artificial...

   Open-source artificial intelligence is an AI system that is freely available to use, study, modify, and share. [1] 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 ]

3. OpenCog - Wikipedia

   en.wikipedia.org/wiki/OpenCog

   OpenCog is a project that aims to build an open source artificial intelligence framework. OpenCog Prime is an architecture for robot and virtual embodied cognition that defines a set of interacting components designed to give rise to human-equivalent artificial general intelligence (AGI) as an emergent phenomenon of the whole system. [2]

4. Comparison of deep learning software - Wikipedia

   en.wikipedia.org/wiki/Comparison_of_deep...

   The following tables compare notable software frameworks, libraries, and computer programs for deep learning applications. Deep learning software by name [ edit ]

5. List of artificial intelligence projects - Wikipedia

   en.wikipedia.org/wiki/List_of_artificial...

   Neuroph, a Java neural network framework. [88] OpenCog, a GPL-licensed framework for artificial intelligence written in C++, Python and Scheme. [15] PolyAnalyst: A commercial tool for data mining, text mining, and knowledge management. [89] RapidMiner, an environment for machine learning and data mining, now developed commercially. [90]

6. Orange (software) - Wikipedia

   en.wikipedia.org/wiki/Orange_(software)

   Orange is an open-source software package released under GPL and hosted on GitHub.Versions up to 3.0 include core components in C++ with wrappers in Python.From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework.

7. Business analytics - Wikipedia

   en.wikipedia.org/wiki/Business_analytics

   In other words, business intelligence focusses on description, while business analytics focusses on prediction and prescription. [1] Business analytics makes extensive use of analytical modeling and numerical analysis, including explanatory and predictive modeling, [2] and fact-based management to drive decision making.