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In this episode, I am with Dr. Charles Martin from Calculation Consulting a machine learning and data science consulting company based in San Francisco. We speak about the nuts and bolts of deep neural networks and some impressive findings about the way they work.\xa0\nThe questions that Charles answers in the show are essentially two:\nWhy is regularisation in deep learning seemingly quite different than regularisation in other areas on ML?\nHow can we dominate DNN in a theoretically principled way?\n\xa0\nReferences\xa0\n\n\nThe WeightWatcher tool for predicting the accuracy of Deep Neural Networks\xa0https://github.com/CalculatedContent/WeightWatcher\n\nSlack channel https://weightwatcherai.slack.com/\n\n\nDr. Charles Martin Blog\xa0http://calculatedcontent.com\xa0and channel\xa0https://www.youtube.com/c/calculationconsulting\n\n\nImplicit Self-Regularization in Deep Neural Networks: Evidence from Random Matrix Theory and Implications for Learning - Charles H. Martin, Michael W. Mahoney