Intelligent-Analytics-Artificial-Neural-Networks

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

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.

Uniform Manifold Approximation and Projection

A python library for decision tree visualization and model interpretation.

A collection of research papers and software related to explainability in graph machine learning.

For extensive instructor led learning

Interpretability and explainability of data and machine learning models

Extensible, parallel implementations of t-SNE

:red_circle: MiniSom is a minimalistic implementation of the Self Organizing Maps

Model explainability that works seamlessly with ? transformers. Explain your transformers model in just 2 lines of code.