Interpretability of Deep Learning Models with Tensorflow 2.0
Did you ever wait long hours for training that turns out unsuccessful? I have, and not just once. So I began looking into tools that could help me anticipate and debug neural networks that come out of training.

This article is a summary of what I found:

A visual logging to check pipeline integrity

Interpretability methods to have insights on the inside of neural networks

All the methods presented in this article are implemented in tf-explain, a library built for interpretability with TensorFlow 2.0. Check out the introduction!

Let’s look now at those implementations.
['Raphaël Meudec']
['Deep Learning', 'Interpretability', 'Machine Learning', 'TensorFlow']