Google Research

Material for gpu-mode lectures

12 Weeks, 24 Lessons, AI for All!

Unified Training of Universal Time Series Forecasting Transformers

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

🎁 6,500,000+ Unsplash images made available for research and machine learning

The repository provides code for running inference with the Meta Segment Anything Model 2 (SAM 2), links for downloading the trained model checkpoints, and example notebooks that show how to use th…

Solve puzzles. Learn CUDA.

Code for Machine Learning for Algorithmic Trading, 2nd edition.

Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…

OpenMMLab course index and stuff

Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022

Performance analysis of predictive (alpha) stock factors

StarNet

此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。

This implementation contains the application of GPlearn's symbolic transformer on a commodity futures sector of the financial market.

WSDM2022留存预测挑战赛 第1名解决方案

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。

Based on “Fast Texture Synthesis using Tree-structured Vector Quantization” and “Multiresolution Sampling Procedure for Analysis and Synthesis of Texture Images” papers

A PyTorch implementation of PointRend: Image Segmentation as Rendering

Class of High Performance Computing taken at U.T.P 2017