motta's Projects
Homework 3
Python library for adversarial machine learning, attacks and defences for neural networks, logistic regression, decision trees, SVM, gradient boosted trees, Gaussian processes and more with multiple framework support
Comparison between Contrastive, Triplet and Yukawa loss functions
Creating layers, back and forward passes with numpy
Studying the L2-norm and other metrics in experiments with correct and random labeling of MNIST data
Scripts to simplify data prepping for Mozilla DeepSpeech.
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch
Generative Adversarial Networks
This is the public repository for the gevolution cosmological N-body code.
EPFL: Applied Data Science 2016
Resources for "Introduction to Deep Learning" course.
Linear models and optimization
Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"
mnist digit classification