September 2024

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

Have you ever wondered how your favorite apps predict what you’ll like next or how cars can now drive themselves? Well, behind the magic of all these cutting-edge technologies lies something truly fascinating: machine learning and its powerful cousin, deep learning. Today, these aren’t just buzzwords but real-world tools transforming industries like healthcare, finance, and

“Data, data, data! I can’t make bricks without clay!” said Sherlock Holmes in one of his mysteries. Today, we may have tons of data, but in many critical fields like cybersecurity, manufacturing, and healthcare, that data often hides a dangerous problem: anomalies. These are the outliers—the misbehaving machines, the fraudulent transactions, the irregular medical scans—that

“Have you ever taken a photo on a foggy day and wondered why everything looks so washed out and blurry? That’s the power of haze—and not in a good way.” Problem StatementHaze, as simple as it seems, is a massive hurdle in many industries, from autonomous vehicles navigating through foggy streets to security cameras trying

Let’s face it, representing high-dimensional data in machine learning models can feel like a never-ending puzzle. Whether you’re working with text or categories, encoding this data for machine learning models is no small feat. You might be thinking: “How do I turn words into something a machine can understand?” This is where embeddings come to