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Kernels!

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