- Barcelona, Spain
- https://fwilhelmi.github.io/
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Track and predict the energy consumption and carbon footprint of training deep learning models.
This repo includes all material for ITU-T FG AN Build-a-thon in 2022 and 2023
Notes, examples, and Python demos for the 2nd edition of the textbook "Machine Learning Refined" (published by Cambridge University Press).
A flexible Federated Learning Framework based on PyTorch, simplifying your Federated Learning research.
Ultimate Solidity, Blockchain, and Smart Contract - Beginner to Expert Full Course | Python Edition
BlockSim: An Extensible Simulation Tool forBlockchain Systems
Master programming by recreating your favorite technologies from scratch.
🔬 Research Framework for Single and Multi-Players 🎰 Multi-Arms Bandits (MAB) Algorithms, implementing all the state-of-the-art algorithms for single-player (UCB, KL-UCB, Thompson...) and multi-play…
Restoring ancient text using deep learning: a case study on Greek epigraphy.
Hands-on Deep Reinforcement Learning, published by Packt
A toolkit for developing and comparing reinforcement learning algorithms.
Spatial Flexible Continuous Time Markov Network (SFCTMN)
DistanceRing Exponential Stations Generator (DRESG) framework.