PyTorch implementations of several SOTA backbone deep neural networks (such as ResNet, ResNeXt, RegNet) on one-dimensional (1D) signal/time-series data.

Finetune Llama 3.3, DeepSeek-R1 & Reasoning LLMs 2x faster with 70% less memory! 🦥

State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

oneAPI Deep Neural Network Library (oneDNN)

Examples for https://github.com/optuna/optuna

🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations

Newton and Quasi-Newton optimization with PyTorch

A curated list of amazingly awesome Home Assistant resources.

"Neural Combinatorial Optimization with Reinforcement Learning"[Bello+, 2016], Traveling Salesman Problem solver

Awesome machine learning for combinatorial optimization papers.

Predicting how the stock market will perform is one of the most difficult things to do. There are so many factors involved in the prediction – physical factors vs. psychological, rational and irrat…

Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlation…

Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity

Implementation of code snippets, exercises and application to live data from Machine Learning for Asset Managers (Elements in Quantitative Finance) written by Prof. Marcos López de Prado.