The DeepCausality project was recently accepted into the Linux Foundation for AI & Data and, as the main author of the project, I want to use the occasion to share a brief introduction. What is computational causality? Although deep learning roots in statistics, popular deep learning frameworks such as TensorFlow or PyTorch shield developers from the underlying math. However, statistics uses correlation under the hood to map an input (say, a question) to an output (an answer).
Sept 22, 2023, JetBrains, the premier software development tool provider, has granted a free all-product license under its open-source community support program to the DeepCausality project. The project team expresses its gratitude towards JetBrains generous contribution. JetBrains Key Links Website OSS Community support Blog DeepCausality Key Links Website GitHub Blog Attributions: Copyright © 2000-2023 JetBrains s.r.o. JetBrains and the JetBrains logo are registered trademarks of JetBrains s.r.o. Copyright © 2000-2023 DeepCausality - The DeepCausality Authors.
LF AI & Data Foundation, the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI) and data open source projects, today announced DeepCausality as its latest Sandbox Project. DeepCausality is an advanced hyper-geometric computational causality library tailored for the Rust programming language. The library is engineered to overcome the limitations of conventional deep learning models by focusing on fast and deterministic context-aware causal reasoning. DeepCausality integrates hypergeometric recursive causal models and end-to-end explainability, creating a robust framework for various industries.
Overview Overview DeepCausality v.0.6 now supports multiple contexts. The previous context API remains the same, meaning all existing code should compile as before. However, new API functionality was added to interact with additional contexts to enable more advanced use cases. Problem To deal with problems in the financial industry when modelling synthetics such as future spreads, multiple contexts apply to the instrument. Specifically, when modelling a classic long-short-term spread, in total three contexts apply, one for the long-term future contract, a second one for the short-term contract, and a final one for the resulting spread.
Why Rust for machine learning? The Good The Not-So-Great Python! The Ugly Conclusion About This post briefly summarizes my current thoughts on machine learning in Rust. I wonder how these may change five years from now. Why Rust for machine learning? Those companies that moved their ML production to Rust see more benefits than just a 25X speedup. Others do the obvious: interfacing Rust with C++ code since most ML libraries are still solid C++ implementations.
ArrayGrid - A Faster Tensor For Low Dim. Data Problem Solution Index Storage API Storage Implementation Grid Type ArrayGrid Usage About ArrayGrid - A Faster Tensor For Low Dim. Data DeepCausality allows fast and efficient adjustment of all values stored in a context hyper-graph. Often, this requires the formulation of an adjustment matrix. The matrix can already be attached to each element of the context graph but may require periodic updates depending on the required changes.
Welcome to our first blog post. About DeepCausality is a hyper-geometric computational causality library that enables fast and deterministic context-aware causal reasoning in Rust. Please give us a star on GitHub.