This section is dedicated to my developed Machine Learning algorithms. Each algorithm listed below links to its corresponding GitHub repository and documentation.

R-MTGB-Robust Multi-Task Gradient Boosting (R-MTGB)

A robust and scalable multi-task learning (MTL) framework that integrates outlier task detection into a structured gradient boosting process. Built with Python and scikit-learn, R-MTGB is designed to generalize well across heterogeneous task sets and is resilient to task-level noise.

GB-DNNR-Gradient Boosted - Deep Neural Network Regression.

GB-DNNR is the Python library for working with Gradient Boosted - Deep Neural Network Regression (GBDNNR).

MT-GB-Multi-Task Gradient Boosting

The provided algorithm contains an implementation of the Multi-Task Gradient Boosting (MT-GB) algorithm, which extends the popular Gradient Boosting method for both classification and regression problems.

GB-CNN-A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks

GB-CNN is the python library for working with Gradient Boosted - Convolutional Neural Networks (GB-CNN) and Gradient Boosted Deep Neural Networks (GB-DNN).

  1. GB-CNN is designed to implement the Gradient Boosted Convolutional layers for the image datasets.
  2. GB-DNN is designed to implement the Gradient Boosted Dense layers for the big dimension tabular datasets.

GBNN-Gradient Boosted Neural Network

GBNN is a python library for dealing with classification (Binary and multi-class) and regression problems.