What is DeepCausality?
DeepCausality is a hyper-geometric computational causality library that enables fast and deterministic context aware causal reasoning over complex multi-stage causality models. Deep Causality adds only minimal overheads, and thus is suitable for real-time applications without additional acceleration hardware.
Examples where DeepCausality can be used include dynamic control systems in the IoT industry, dynamic monitoring systems in the cloud industry, and dynamic market models in the financial industry. Start-ups aiming to disrupt existing industries may also explore DeepCausality to gain a competitive edge.
See the documentation for more.
See the blog for latest updates.
DeepCausality is hosted as a sandbox project at the Linux Foundation for data and artificial intelligence. The Linux Foundation is the world’s leading home for collaboration on open source software, hardware, standards, and data. Linux Foundation projects are critical to the world’s infrastructure including Linux, Kubernetes, Node.js, ONAP, RISC-V, SPDX, OpenChain, and more. The Linux Foundation focuses on advancing best practices and addressing the needs of contributors, users, and solution providers to create sustainable models for open collaboration. For more information, please visit linuxfoundation.org.
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