In this section, we have the public talks, seminars, workshops, and interviews delivered by MLDawn.
What is this post about? In this super gentle 101 talk given at the Insight Centre for Data Analytics at University College Dublin , I have gone through some of the most mportant aspects of Artificial Neural Networks (ANNs): Here are what you will learn in this talk along with the corresponding time-stamps: 00:33 Table …
We never Had Truly Understood the Bias-Variance Trade-off!!! In this interview with Prof. Mikhail Belkin, we will discuss his amazing paper: “Reconciling modern machine learning practice and the bias-variance trade-off” which introduces the other half of the bias-variance trade-off that we have been missing all these years. Behold the Double-Descent behavior! In this exciting interview …
“There are a lot more papers written than there are widely read!” (Tom Mitchell) Prof. Tom Mitchell is one of the giants of machine learning and artificial intelligence, who started the world’s first machine learning department at Carnegie Mellon University (CMU). Prof. Mitchell has kindly agreed to an interview with us at MLDawn, presented below …
ECML-PKDD-2019 on EBFDD networks for Anomaly Detection This paper introduces the Elliptical Basis Function Data Descriptor (EBFDD) network, a one-class classification approach to anomaly detection based on Radial Basis Function (RBF) neural networks. The EBFDD network uses elliptical basis functions, which allows it to learn sophisticated decision boundaries while retaining the advantages of a shallow …
ECML-PKDD-2019: Elliptical Basis Function Data Descriptor (EBFDD) for Anomaly Detection Read More »
This talk was delivered in PyData meetup. This was an amazing meetup with great talks and ideas being discussed! Thanks to organizers, I was also invited to give a talk. Below is the summary: In this talk we learn about what Artificial Neural Networks (ANNs) are, and find out how in general, Maximum Likelihood …
Birth of Error Functions in Artificial Neural Networks Read More »
