Today’s world is fascinated by the world of AI, in particular, generative models such as the many large language models pioneered by the biggest tech companies in the world. Although these advancements are exciting and have thrown AI more into the mainstream, I still have a soft spot for the classical machine learning techniques, which can be very powerful when used correctly.
I previously wrote an article detailing the high-level concepts of SVMs. In this article, I am going to explain the intuitive maths behind the algorithm and show why it is so powerful.
Apologies for the pictures, Medium’s lack of support for LaTeX is frustrating…
When I first encountered Support Vector Machines (SVMs), I glossed over the mathematics, choosing to dive into their practical applications for solving real-world problems. Looking back, that was a mistake. A deep understanding of any machine learning topic depends on grasping its mathematical foundations. If you’ve been sidestepping the math behind SVMs, I hope this article has revealed how intuitive it can be. Embracing the math will transform your understanding.