A Quick Look at Support Vector Machines

One thing that I discovered recently which surprised me (while taking the Udacity SDC)is how effective and resilient these "older" ML algorithms can be. Neural networks was always my go to method for most of my classification or regression problems for my small side projects. But now I learned with the minimal dataset I have (<5K samples), linear regression, SVM, or decision tress is the way to go. I got higher accuracy and it's about 10X faster in terms of computational time!

Aaah, I was hoping for an explanation of the kernel trick. I think that is the hardest concept in support vector machines.

Very cool! However, I think the author should have spent a a few more words and figures to distinguish support vector machines from standard perceptrons. Maximum margin classification and the definition of 'support vectors,' in my experience, helps demystify the algorithm.

This is great! Any follow-ups describing kernels?

Amazingly well written. Short and to the point, humbly sharing something cool!

Many aspects of Machine Learning boil down to optimization problems.

well, that really was a quick look. Any reading-recommendations about the kernel-functions? How do they work and why are they fast?

Best name for a blog ever?