Listening to the neural network gradient norms during training
Training neural networks is often done by measuring many different metrics such as accuracy, loss, gradients, etc. This is most of the time done aggregating the...
Category: Machine Learning
Uncertainty Estimation in Deep Learning (PyData Lisbon / July 2019)
Just sharing some slides I presented at the PyData Lisbon on July 2019 about the talk “Uncertainty Estimation in Deep Learning“: Uncertainty Estimat...
Benford law on GPT-2 language model
I wrote some months ago about how the Benford law emerges from language models, today I decided to evaluate the same method to check how the GPT-2 would behave ...
Randomized prior functions in PyTorch
I was experimenting with the approach described in “Randomized Prior Functions for Deep Reinforcement Learning” by Ian Osband et al. at NPS 2018, wh...
PyData Montreal slides for the talk: PyTorch under the hood
These are the slides of the talk I presented on PyData Montreal on Feb 25th. It was a pleasure to meet you all ! Thanks a lot to Maria and Alexander for the inv...
A sane introduction to maximum likelihood estimation (MLE) and maximum a posteriori (MAP)
It is frustrating to learn about principles such as maximum likelihood estimation (MLE), maximum a posteriori (MAP) and Bayesian inference in general. The main ...
Introducing EuclidesDB – A machine learning feature database
Past week I released the first public version of EuclidesDB. EuclidesDB is a multi-model machine learning feature database that is tightly coupled with PyTorch ...
Análise bayesiana dos microdados do ENEM do Rio Grande do Sul
* This post is in Portuguese. It’s a bayesian analysis of a Brazilian national exam. The main focus of the analysis is to understand the underlying factor...