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Learn quantitative finance with this comprehensive lecture series. Adapted from the Quantopian Lecture Series. Uses free sample data.
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚
Lecture notes for "Fast Algorithms and Integral Equations"
A simple & elegant experiment tracking framework that integrates persistence logic & best practices directly into Python
A Python framework for high performance GPU simulation and graphics
A lightweight macOS window and app manager scriptable with JavaScript
OneSweep, implemented in CUDA, D3D12, and Unity style compute shaders. Theoretically portable to all wave/warp/subgroup sizes.
Implementation of various Parallel Computing algorithms using CUDA C++
A nearly complete collection of prefix sum algorithms implemented in CUDA, D3D12 and Unity. Theoretically portable to all wave/warp/subgroup sizes.
CUDA implementation of exclusive prefix sum via Blelloch's algorithm
Annotations of the interesting ML papers I read
Implementation of FlashAttention in PyTorch
cuda implementation of depthwise conv3d
Perplexica is an AI-powered search engine. It is an Open source alternative to Perplexity AI
Multi-node production AI stack. Run the best of open source AI easily on your own servers. Create your own AI by fine-tuning open source models. Integrate LLMs with APIs. Run gptscript securely on …
✨✨Latest Advances on Multimodal Large Language Models
A State-Space Model with Rational Transfer Function Representation.
Programmable, reproducible, interactive documents
Experts.js is the easiest way to create and deploy OpenAI's Assistants and link them together as Tools to create advanced Multi AI Agent Systems with expanded memory and attention to detail.
data-to-paper: Backward-traceable AI-driven scientific research
OpenUI let's you describe UI using your imagination, then see it rendered live.
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
Efficient implementations of state-of-the-art linear attention models in Pytorch and Triton