This is a paper breakdown video of the paper: Kolmogorov Arnold Networks, which brilliantly provides an alternative to standard Multi Layer Perceptrons. The video discusses the main contributions and core ideas of the paper, visually explaining the math, concepts, and challenges ahead. #deeplearning #machinelearning #neuralnetworks To access the animations, narration scripts, slides, notes, etc for the video, consider joining us on Patreon. Patreon: Buy me a coffee at ! Timestamps: 0:00 - Intro 1:03 - Kolmogorov Arnold Representation Theorem 5:05 - KAN Layers 8:00 - Comparisons 9:00 - B-splines 11:08 - Grid Extension, Sparsification, Continual Learning 14:00 - KANs get the best of MLPs and Splines 15:00 - Advantages and Challenges for KANs Check out the paper: Check out code: and