Amit Yadav

Optimization might sound like a buzzword, but it’s one of those things that silently drives almost everything around you—whether it’s finding the quickest route on your GPS or maximizing the efficiency of a machine learning model. What is Optimization? So, what exactly is optimization? In simple terms, optimization is all about finding the “best” solution […]

“Optimization is the silent engine behind machine learning—without it, even the most sophisticated models would remain lifeless equations.” When you’re working with machine learning, optimization algorithms are your behind-the-scenes power tools. They drive the process of turning raw data into models that can make predictions, classify images, or even generate human-like text. Think of it

Imagine this: You’re running a deep neural network on a mobile device, but it’s too slow, drains battery life, and takes up too much memory. Here’s the deal: while deep learning models have revolutionized industries from healthcare to self-driving cars, they’re often overloaded with millions—even billions—of parameters. Many of these parameters, however, are doing little

“In the world of deep learning, bigger isn’t always better. What if I told you that you could trim down your model, cutting out the ‘fat,’ and still maintain—or even improve—its performance?” Let’s face it: Deep learning models can be huge. From neural networks with millions of parameters to massive architectures like BERT or GPT,

Imagine this: you’ve set up the perfect marketing campaign, driving traffic to your website, but at the end of the day, your conversion rate is still not where you want it to be. What gives? This is where conversion rate prediction (CRP) comes into play, helping you forecast which users are most likely to convert,

You know the saying, “The only constant is change.” Nowhere is that truer than in the field of AI, particularly when it comes to how we extract meaning from large sets of data. If you’re in the business of discovering patterns in text, you’ve probably come across topic modeling—the process of uncovering the hidden themes

Have you ever wondered how e-learning platforms know what a student understands at any given moment? In the world of education, it’s not enough to just assess what a student knows today—what really matters is predicting how their knowledge will evolve over time. This is where knowledge tracing comes into play. It helps educational systems

Imagine looking at a photo—what’s the first thing your brain does? It recognizes objects, colors, and even the depth of the scene. This is how our brains process visual representations, and in many ways, computer vision tries to mimic this ability. But here’s the catch: computers need a lot of help to “see” the world

“You can have data without information, but you cannot have information without data.” – Daniel Keys Moran. When it comes to natural language processing (NLP), the same principle applies: you can have a sentence, but without understanding its meaning, that sentence is just a string of words. That’s where sentence embeddings come into play. They

You know, in an ideal world, we’d have all the data we need neatly labeled and organized, ready for our models to chew through. But here’s the real challenge: How do we ground phrases in images when we don’t have the luxury of detailed, pixel-perfect labels? In weakly supervised settings, this is the kind of