Popular GitHub Repositories for Selection Sort

Collection of various algorithms in mathematics, machine learning, computer science and physics implemented in C++ for educational purposes.

Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.

A Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.

Visual analysis and diagnostic tools to facilitate machine learning model selection.

Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.

This is originally a collection of papers on neural network accelerators. Now it's more like my selection of research on deep learning and computer architecture.

A selection of state-of-the-art research materials on trajectory prediction

Deep Learning and Machine Learning stocks represent promising opportunities for both long-term and short-term investors and traders.

Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.

A collection of computer vision pre-trained models.

:rocket: Blazing fast neuro-evolution & backpropagation for the browser and Node.js

High-performance multiple object tracking based on YOLO, Deep SORT, and KLT ?

Automated Deep Learning without ANY human intervention. 1'st Solution for AutoDL challenge@NeurIPS.

BoT-SORT: Robust Associations Multi-Pedestrian Tracking

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

Multi-camera live traffic and object counting with YOLO v4, Deep SORT, and Flask.

[CVPR2023] The official repo for OC-SORT: Observation-Centric SORT on video Multi-Object Tracking. OC-SORT is simple, online and robust to occlusion/non-linear motion.

EvalML is an AutoML library written in python.

A comprehensive library for machine learning and numerical computing. Apply Machine Learning with Rust leveraging first principles.

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.