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Exercises and exampled developed during the lectures
A simple tutorial of Diffusion Probabilistic Models
A collection of resources and papers on Diffusion Models
A minimal yet resourceful implementation of diffusion models (along with pretrained models + synthetic images for nine datasets)
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
MIT IAP short course: Matrix Calculus for Machine Learning and Beyond
Pilot course for Robotics 101: Computational Linear Algebra
Cloud Computing Course at the FH Campus Wien
The Generative Landscape - a course on generative modelling (currently unfinished). Join our Discord: https://discord.gg/vSjhr8xb4g
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
Must read research papers and links to tools and datasets that are related to using machine learning for compilers and systems optimisation
Modern C++ Programming Course (C++03/11/14/17/20/23/26)
Python Data Science Handbook: full text in Jupyter Notebooks
Material for The Mathematical Engineering of Deep Learning. See https://deeplearningmath.org
T81-558: PyTorch - Applications of Deep Neural Networks @Washington University in St. Louis
Modern Computer Vision with PyTorch, published by Packt
This is a wikisitory meant to provide some core guidelines on what to do after installing a GNU/Linux distribution from scratch. Forked from https://gitlab.igg.uni-bonn.de/teaching/must-have-tools …
Just a starter project to start coding in C++
This repository contains the slides of the FAU lecture Pattern Recognition
DevOps & Cloud Infrastructure Course (UST SEIS 615)
ICCV 2023 Papers: Discover cutting-edge research from ICCV 2023, the leading computer vision conference. Stay updated on the latest in computer vision and deep learning, with code included. ⭐ suppo…