mlsquare is an open source, deep tech initiative to democratize ML. The fundamental tenet is, use ML to make ML useful.

Past ML software projects

WIP ML software projects

  • FedEm: a framework to decentralize the development of LLMs

Upcoming ML software projects

  • neural tokenizers to enable the extension of English-centric LLMs to other languages

  • model grafting via analytical and numerical approximations to enable efficient cross-architecture transfer learning

  • compute & data efficient training via layer-by-layer and block-by-block LLM training, which is inherently embarrassingly parallel, and with potential analytical update rules (no need for backprop and gradients)

  • deep kernel machines a framework for composing many transformer-like architectures with specifiable inductive biases

  • xKANs a framework for composing many KAN-like architectures with specifiable non-parametric function approximators like cubic B-splines, Chebyshev polynomials, Wavelets, etc..

  • embeddings for tabular data bridge the gap between tabular and modern deep learning models, and make deep learning architectures perform no worse than tree-based models like the battle tested catboost

Upcoming Courses

  • MLOps: Theory and Practice for upper level undergraduate students

  • Theory of Deep Learning: Why it works - A Constructionist Approach for upper level undergraduate students

  • Ilya Sutskever’s 30 papers - Walk through

Yours openly
The Saddle Point

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