TensorFlow demonstration of the Knowledge Distillation framework to show how soft labels act as regularizers and a neural network’s knowledge can be transfered to a simpler model
PyTorch Implementation with sklearn-like API of a Neural Network that constructs a Boolean Function by dividing the feature space with convex polytopes.
Machine Learning implementations from scratch. Using minimal dependencies this collection intends to cover fundamental machine learning algorithms: from linear regression to neural networks
A Python implementation of the Gaussian Processes framework with Bayesian Optimization. Fit noiseless or noisy data and use existing or custom kernels. Bayesian optimization module using existing acquisition functions (μ+kσ,...
Minimal Python implementation of the Dynamic Movement Primitives (DMPs) framework for the description of demonstrated trajectories with dynamical systems.