Build Your own Deep Learning Framework - A Hands-on Introduction to Automatic Differentiation - Part 2

In the previous part of this series, we learned about forward mode automatic differentiation, and we saw its limitations when we need to calculate the gradient of a function of many variables. Today, we’ll into another mode of automatic differentiation... Read more about Build Your own Deep Learning Framework - A Hands-on Introduction to Automatic Differentiation - Part 2

Machine Learning Theory - Part 3: Regularization and the Bias-variance Trade-off

In first part we explored the statistical model underlying the machine learning problem, and used it to formalize the problem in terms of obtaining the minimum generalization error. By noting that we cannot directly evaluate the generalization error of an... Read more about Machine Learning Theory - Part 3: Regularization and the Bias-variance Trade-off