Amit Yadav

“In the world of data, how you present information can make or break your results.”When it comes to machine learning, the way we encode categorical data is critical—it’s like laying the foundation for your model’s success. Now, if you’re working with real-world data, you’re bound to run into categories—things like colors, product types, or even […]

Have you ever heard the saying, “Not everything that counts can be counted?” Well, in machine learning, everything must be counted—especially categorical data. That’s where one-hot encoding and dummy variables come into play. Brief Overview: If you’ve worked with machine learning models, you’ve likely come across categorical data—features that represent distinct categories rather than continuous

What is Encoding in Machine Learning? “In life, everything is about translation—sometimes between languages, other times between thoughts and actions. In machine learning, we’re constantly translating too—only here, we’re turning categories into numbers.” When you’re working with machine learning models, one thing you’ll come across often is categorical data—you know, things like colors, job titles,

Imagine walking into a bookstore, and instead of searching through hundreds of titles, the store instantly presents you with five books you’re likely to love. That’s the magic of recommender systems, and they’re everywhere—from your Netflix queue to the suggested products on Amazon. But here’s the catch: providing personalized recommendations isn’t as easy as it

“Data is the new oil”—this phrase couldn’t be more accurate in today’s AI-driven world. But what happens when your model needs oceans of labeled data to perform, and you have barely a drop? This is where label-efficient learning becomes a lifesaver, especially for complex tasks like semantic segmentation.” Semantic segmentation is no ordinary task. Imagine

Imagine you’re browsing your favorite e-commerce platform, and you notice the suggestions seem to evolve based on what you’ve just looked at or purchased. Whether it’s your streaming service offering a lineup of shows after a binge or a music app predicting your next favorite song, these are the magic moments powered by sequential recommendation

Let’s start with something simple but powerful: contrastive learning. It might sound fancy, but the core idea is straightforward. It’s a method used in self-supervised learning where the goal is to learn useful representations of data—without needing labeled examples. What’s the secret sauce? Contrast! In contrastive learning, you teach the model to pull similar things

Let’s kick things off with a strong opening. When it comes to categorical data, translating words, labels, or categories into a format that machines can understand is a critical step. Machine learning models, after all, only comprehend numbers. This might surprise you: one of the simplest yet most effective ways to accomplish this is through

“Learning is not attained by chance, it must be sought for with ardor and attended to with diligence.”– Abigail Adams Now, what if I told you that machines could learn in ways that mimic how we, as humans, understand and make sense of the world—by comparing and contrasting information? Welcome to the fascinating world of

Imagine this: A computer that learns to play a game so well that it not only beats the world’s best human players, but also devises new strategies that no human ever considered. You’ve probably heard of AlphaGo—DeepMind’s famous AI that defeated the world champion Go player back in 2016. But here’s the fascinating part: the