Machine learning, in numpy
- machine-learning
- neural-networks
- topic-modeling
- gaussian-mixture-models
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Machine learning, in numpy
Automated Machine Learning with scikit-learn
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies
A modular active learning framework for Python
SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization
Naive Bayesian Classification for Golang.
A general classifier module to allow Bayesian and other types of classifications. A fork of cardmagic/classifier.
Python package for Bayesian Machine Learning with scikit-learn API
Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving
A Naive Bayes machine learning implementation in Elixir.
Artificial intelligence/machine learning data structures and Swift algorithms for future iOS development. bayes theorem, neural networks, and more AI.
A hyperparameter optimization framework, inspired by Optuna.
Bayesian text classifier with flexible tokenizers and storage backends for Go
A robust, full-featured Ruby implementation of Naive Bayes
An English (Porter2) stemming implementation in Elixir.
Accurate Bayesian sentence tokenizer in Ruby.
Ruby Scoring API for PMML
Gaussian processes and Bayesian optimization for images and hyperspectral data
Simple Naive Bayes classifier