Angelo Gnazzo
angelognazzo
PhD @ TU Wien - AITHYRA | Graphs, Geometry, AI4Science & Generative AI | MSc in Mathematics @ ETH Zurich
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Top Repositories
Implementation of targeted (FGSM targeted) and untargeted (FGSM untargeted) FGSM attack and of PGD attack for MNIST trained neural network model
Repository that contains the projects of the Probabilistic Artificial Intelligence class offered in Fall 2021 at ETH Zurich
Repository for the Reliable and Trustworthy AI course offered in Fall 2022 at ETH Zürich: implementation of DeepPoly, Robustness Analyzer for Deep Neural Networks
Repository for the Deep Learning in Scientific Computing course offered in Spring 2022 at ETH Zürich: Deep learning project with the aim of studying the preliminary design of a thermal energy storage.
ProbPy is a comprehensive repository dedicated to providing an extensive collection of probability puzzles, riddles, and solutions typically encountered in data science and quantitative research interviews.
Code samples and documentation for SmartNoise differential privacy tools
Repositories
10Repository that contains the projects of the Probabilistic Artificial Intelligence class offered in Fall 2021 at ETH Zurich
Implementation of targeted (FGSM targeted) and untargeted (FGSM untargeted) FGSM attack and of PGD attack for MNIST trained neural network model
ProbPy is a comprehensive repository dedicated to providing an extensive collection of probability puzzles, riddles, and solutions typically encountered in data science and quantitative research interviews.
Repository for the Reliable and Trustworthy AI course offered in Fall 2022 at ETH Zürich: implementation of DeepPoly, Robustness Analyzer for Deep Neural Networks
Repository for the Deep Learning in Scientific Computing course offered in Spring 2022 at ETH Zürich: Deep learning project with the aim of studying the preliminary design of a thermal energy storage.
Code samples and documentation for SmartNoise differential privacy tools
Config files for my GitHub profile.
No code here :) study of generalization error bounds using mathematical analysis, probability and information theory
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