Understanding Positive Semi-Definite Kernels and Their Applications

Today Yannic Lightspeed Kilcher and I spoke with Alex Stenlake about Kernel Methods. What is a kernel? Do you remember those weird kernel things which everyone obsessed about before deep learning? What about Representer theorem and reproducible kernel hilbert spaces? SVMs and kernel ridge regression? Remember them?! Hope you enjoy the conversation!

00:00:00 Tim Intro

00:01:35 Yannic clever insight from this discussion 

00:03:25 Street talk and Alex intro 

00:05:06 How kernels are taught

00:09:20 Computational tractability

00:10:32 Maths 

00:11:50 What is a kernel? 

00:19:39 Kernel latent expansion 

00:23:57 Overfitting 

00:24:50 Hilbert spaces 

00:30:20 Compare to DL

00:31:18 Back to hilbert spaces

00:45:19 Computational tractability 2

00:52:23 Curse of dimensionality

00:55:01 RBF: infinite taylor series

00:57:20 Margin/SVM 

01:00:07 KRR/dual

01:03:26 Complexity compute kernels vs deep learning

01:05:03 Good for small problems? vs deep learning)

01:07:50 Whats special about the RBF kernel

01:11:06 Another DL comparison

01:14:01 Representer theorem

01:20:05 Relation to back prop

01:25:10 Connection with NLP/transformers

01:27:31 Where else kernels good

01:34:34 Deep learning vs dual kernel methods

01:33:29 Thoughts on AI

01:34:35 Outro