Python Tutorial: Image Compression using Numpy
Lately, I have been working on a project that involves sending images, captured by an industrial camera, from one station to another. The task itself is trivial, however, if the…
Lately, I have been working on a project that involves sending images, captured by an industrial camera, from one station to another. The task itself is trivial, however, if the…
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…
When we work with images and real-world objects, we want to collect their coordinates and project them onto a plane for simplification. Such applications are useful in areas like self-driving…
"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…