Popular GitHub Repositories for Threshold Selection

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.

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

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

A collection of computer vision pre-trained models.

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

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

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.

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.

Auto Tune Models - A multi-tenant, multi-data system for automated machine learning (model selection and tuning).

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

[CVPR 2019 Oral] Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation

A scikit-learn-compatible Python implementation of ReBATE, a suite of Relief-based feature selection algorithms for Machine Learning.

本人多次机器学习与大数据竞赛Top5的经验总结，满满的干货，拿好不谢

ML hyperparameters tuning and features selection, using evolutionary algorithms.

Data search & enrichment library for Machine Learning → Easily find and add relevant features to your ML & AI pipeline from hundreds of public and premium external data sources, including open & commercial LLMs