A collection of various deep learning architectures, models, and tips

Kolmogorov Arnold Networks

Tutorials and programming exercises for learning Q# and quantum computing

Microsoft Quantum Development Kit Samples

PyTorch Implementation of Physics-informed Neural Networks

Plant Disease Detector Web Application

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Notebooks for introducing the machine learning toolbox MLJ (Machine Learning in Julia)

This tutorial is part of the didactic text: Learning Deep Learning (CDT-15).

Research tools for intermingling gravitational-wave data analysis with numerical relativity

Test cosmological models with Supernova and Quasar data

Exploration of TensorFlow-2 and TensorFlow probability to implement Bayesian Neural Networks, Normalizing flows, real NVPs and Autoencoders. Exploration of Bayesian Modelling and Variational Infere…