## 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...

Skip to content# Category: Machine Learning

## Listening to the neural network gradient norms during training

## Uncertainty Estimation in Deep Learning (PyData Lisbon / July 2019)

## Benford law on GPT-2 language model

## Randomized prior functions in PyTorch

## PyData Montreal slides for the talk: PyTorch under the hood

## A sane introduction to maximum likelihood estimation (MLE) and maximum a posteriori (MAP)

by Christian S. Perone

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...

Just sharing some slides I presented at the PyData Lisbon on July 2019 about the talk “Uncertainty Estimation in Deep Learning“: Uncertainty Estimat...

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 ...

I was experimenting with the approach described in “Randomized Prior Functions for Deep Reinforcement Learning” by Ian Osband et al. at NPS 2018, wh...

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...

It is frustrating to learn about principles such as maximum likelihood estimation (MLE), maximum a posteriori (MAP) and Bayesian inference in general. The main ...