October 2024

Let’s begin with the basics: the Hierarchical Dirichlet Process (HDP) is a non-parametric Bayesian method designed to model grouped data. Now, I know “non-parametric” might sound like something out of a statistics textbook, but don’t worry—I’ve got you covered. What non-parametric means here is that the model doesn’t require you to specify the number of […]

Imagine you’re trying to decode a secret message, but you only have partial information about how it was encoded. What if I told you there’s an algorithm that can help you not just decode the message, but also learn the exact rules of the encoding process as you go? This might surprise you, but that’s

Imagine you’re tasked with translating a sentence from one language to another. You could go word by word, hoping for the best, or you could try a more refined approach, searching for the most likely sequence of words that makes sense. That’s where Beam Search shines. This might surprise you: Beam Search isn’t just about

You know, there’s a famous saying, “All models are wrong, but some are useful.” This perfectly captures the world of approximate Bayesian inference, and that’s where Expectation Propagation (EP) shines. So, what is EP exactly? Imagine trying to understand a massively complicated system (think weather prediction or stock market forecasts), where calculating the exact probabilities

You’ve probably heard that understanding is power, but when it comes to Belief Propagation Neural Networks (BPNNs), understanding goes beyond power—it gives you clarity in some of the most complex systems. Let’s break it down from the start. Belief Propagation (BP) and Probabilistic Graphical Models Belief Propagation is a message-passing algorithm that originated from probabilistic

When you hear “motivation,” you probably think of why we do what we do, right? Well, the same applies to machines—sort of. Reinforcement learning (RL) is all about teaching machines how to make decisions, but here’s where it gets interesting: traditionally, RL relies on external rewards, like a robot getting a point for reaching a

You’ve probably heard the phrase, “Learning by doing.” That’s essentially what Reinforcement Learning (RL) is all about. In RL, an agent (think of it like a robot, or even a piece of software) learns how to perform a task by interacting with an environment. The goal? To maximize some notion of cumulative reward. Here’s the

Let me take you back to where it all begins—statistical mechanics. A Boltzmann Machine is essentially an energy-based model that mimics the way energy works in physical systems. You can think of it as a neural network where each state corresponds to an energy level, and the system’s goal is to minimize that energy. But,

Imagine you’re trying to teach a machine how to see—without ever giving it labeled examples of what it’s looking at. Sound challenging? That’s precisely the hurdle we’re tackling in unsupervised learning, especially when it comes to visual representation. Here’s the deal: labeling data, especially images, is time-consuming, expensive, and often impractical when the dataset scales

You might already be familiar with the importance of robust language models in Natural Language Processing (NLP). But today, we’re going to dive into something that’s a step beyond the ordinary: RoBERTa—Facebook AI’s powerhouse language model that has taken NLP to the next level. This post will cover everything you need to know about RoBERTa,