If you’ve ever tried to explain Bayes’ theorem to someone at a party, you know the look you get. That glazed-over, polite nod that says “I’ll remember your name but…
You know how some people like to claim that the math we learn in high school has no real-world use? That it’s just a bunch of abstract equations you’ll never…
Derivatives are a crucial concept in machine learning. It is a mathematical technique to optimize the model's parameters to reduce the cost on every iteration and is widely used in…
"Optimization is at the heart of machine learning. It is the process of searching through a space of possible models to find one that performs well on a task. It…
Many of us may find understanding statistics a challenging task. Moreover, when it comes to conditional probability, we may find ourselves scratching our heads. And yes, I am one of…
"Machine learning is not magic; it's just a tool, like a hammer or a wrench. And like any tool, it has limitations, which you need to understand in order to…
In this previous post on matrix multiplication, I discussed the geometrical significance of the dot product of matrices and how it represents the linear mapping of vectors. We also realized…
In my last blog on orthogonal projection, I shared how visualizing mathematical concepts help us understand them better. So, I thought of doing the same with matrix multiplication. Matrix multiplication…
Mathematics is always fascinating when we understand it by visualizing its effect. Especially, when you already know something and prove it using mathematics, that 'AHA' moment hits differently. The same…
On my road to understanding linear algebra for the sake of machine learning, linear transformation of vectors is the topic that I found most challenging to understand, at the same…
