M

machine learning

Assess procedures for "feature importance" against true feature importance

Mini-project to study the impact of pure-noise-features on predictive performance in supervised classification

A python tool to prepare Xeno-Canto audio files for machine learning projects

Repository for the RGrid ML recruitment test!

Armadillo: fast C++ library for linear algebra (matrix maths) & scientific computing - https://arma.sourceforge.net

Instance Hardness analysis in Machine Learning

$ mldev | is a data science experiment automation and reproducibility toolkit.

check our experiment templates: https://gitlab.com/mlrep

Welcome to the "AI_Course_Template" - the basic repository template for students enrolled in the Artificial Intelligence course in the Department of Computer Science. This repository is designed to provide a standardized structure and set of guidelines for student projects, facilitating organized development, easy navigation, and consistent documentation.

Key Features:

Structured directory: Includes predefined folders for data (both raw and processed), source code, tests, and documentation, ensuring a clean and organized project workspace.

Comprehensive README Template: A guide to help students effectively document their project overview, goals, installation process, data descriptions, usage instructions, and contribution guidelines.

Resource Hub: Serves as a central location for essential resources, reference materials, and project-specific instructions.

Collaboration Ready: Configured to support collaborative projects, encouraging students to work together efficiently and share their progress.

Purpose:

This template is designed to streamline the project setup process, allowing students to focus more on the innovative aspects of AI and machine learning, rather than the initial setup and organization. By following this template, students will learn the importance of project organization, clear documentation, and consistent coding practices that are essential for any aspiring AI professional.

Beginner projects that help and motivate me to keep learning.

Bandicoot: user-friendly C++ library for GPU accelerated linear algebra, integrating with CUDA and OpenCL

Finally a smart RSS reader which doesn't suck ass or your data.

Extract video representations (semantic, geometric, deep features) for the frames of any video.

From scratch implementation of neural ordinary differential equations with batch-wise GPU enabled ODE solvers.

Bahn-Vorhersage - The best Train Delay Prediction System.

Compute confidence intervals for ranks to compare the results of algorithms.

A landcover classification tool based for humans. Classifier does "traditional" supervised and unsupervised learning. Image segmentation and soon also object detection

Test handbook fork