tsitsimis/gau-pro

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Created almost 7 years ago
Updated over 2 years ago

tsitsimis/gau-pro

tsitsimis

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Gaussian Processes and Bayesian Optimization implemeted in Python

dmpling

Dynamic Movement Primitives in Python

Jupyter Notebook

constrainedlr

Drop-in replacement of sklearn's Linear Regression with coefficients constraints

tinyshap

Python package providing a minimal implementation of the SHAP algorithm using the Kernel method

Jupyter Notebook

underactuated

Python implementations of Optimal Control and Optimization algorithms for simulated underactuated systems and walking robots

from-scratch

Machine Learning implementations from scratch. Using minimal dependencies this collection intends to cover fundamental machine learning algorithms: from linear regression to neural networks

neural-network-distillation

Simple demonstration of the Knowledge Distillation framework with neural networks

Jupyter Notebook

8R-redundant-robotic-arm

8R redundant robotic arm with obstacle avoidance

planar-robot

Basic planar robotic arm with arbitrary number of rotary joints

Jupyter Notebook

disjunctive-normal-networks

PyTorch implementation of Disjunctive Normal Networks

Jupyter Notebook

suits-overused-phrases

Scraping and processing of the TV series Suits transcript to extract most common phrases

Jupyter Notebook

robot-handover

A controller for the humanoid robot NAO for interactive human-to-robot object handovers

novel

Anomaly detection with One-Class SVM in Python from scratch

lazy-stack

Automatically create SQL Alchemy models from Great Expectations suites

Jupyter Notebook