Kernels!
Machine Learning Street Talk (MLST)
00:00
Navigating Kernel Methods and Deep Learning
This chapter explores the intricacies of inverting matrices in kernel methods and the computational challenges posed by large datasets. It contrasts traditional support vector machines (SVMs) with deep learning techniques, highlighting their respective strengths and limitations in various data scenarios. Additionally, the discussion delves into topics such as Bayesian non-parametrics, the significance of interpretability, and the mathematical underpinnings of kernel functions in machine learning.
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