Can we predict the accuracy of a Neural Network? Yes, with the WeightWatcher tool by Charles Martin, Ph.D. - 002

In this Episode we talked about the deep neural networks and the spectral density of each layer's weights. It turns out, you can predict the accuracy ( and many more) with the WeigthWatcher application. We talk about the 5+1 Phases of learning and Heavy Tailed Self Regularization. Charles Martin, PhD on LinkedIn: https://www.linkedin.com/in/charlesmartin14/ During the episode we talked about these VC Theory: https://en.wikipedia.org/wiki/Vapnik%E2%80%93Chervonenkis_theory Why Deep Learning works? post by Charles Martin, 2015 https://calculatedcontent.com/2015/03/25/why-does-deep-learning-work/ Presentation at Berkeley: Why Deep Learning works? by Charles Martin, 2016 https://www.youtube.com/watch?v=fHZZgfVgC8U Several Papers written by Charles Martin and Michael Mahoney: https://arxiv.org/search/?query=%22Charles+H.+Martin%22&searchtype;=author&abstracts;=showℴ=-announced_date_first&size;=50 Newest blog post about weightwatcher, 2019 December: https://calculatedcontent.com/2019/12/03/towards-a-new-theory-of-learning-statistical-mechanics-of-deep-neural-networks/ WeightWatcher on GitHub: https://github.com/CalculatedContent/WeightWatcher easy installation for python users: pip install weightwatcher How to reach out: https://calculationconsulting.com/ [email protected] To access their slack channel please contact Charles first. ---Copyright Info--- Music is from https://filmmusic.io , intro first part is by Miklos Toth and some free garage band loops. :) intro second part: "Aces High" by Kevin MacLeod, outro "Acid Trumpet" by Kevin MacLeod (https://incompetech.com), License: CC BY (http://creativecommons.org/licenses/by/4.0/)