Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
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Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
Go package for computer vision using OpenCV 4 and beyond. Includes support for DNN, CUDA, OpenCV Contrib, and OpenVINO.
Statistical Machine Intelligence & Learning Engine
Python Audio Analysis Library: Feature Extraction, Classification, Segmentation and Applications
Convolutional Neural Network for Text Classification in Tensorflow
An absolute beginner's guide to Machine Learning and Image Classification with Neural Networks
A collection of important graph embedding, classification and representation learning papers with implementations.
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Natural language detection
State of the Art Natural Language Processing
A curated list of action recognition and related area resources
Accelerated deep learning R&D
The machine learning toolkit for time series analysis in Python
A curated list of deep learning image classification papers and codes
List of articles related to deep learning applied to music
Deep neural network to extract intelligent information from invoice documents.
A collection of research papers on decision, classification and regression trees with implementations.
An open source library and framework for deep learning on satellite and aerial imagery.
Collaborate & label any type of data, images, text, or documents, in an easy web interface or desktop app.
A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).
A curated list of data mining papers about fraud detection.
Machine Learning in R
Open decision models from 0.8B to 27B: a calibrated probability for every option in one pass, and a short thought only when unsure. Engine for vLLM, llama.cpp and MLX.