Amit Yadav

“The real voyage of discovery consists not in seeking new landscapes, but in having new eyes.” – Marcel Proust. In today’s fast-paced digital world, we’re witnessing a paradigm shift. Artificial intelligence and machine learning are no longer just buzzwords—they’re transforming industries from healthcare to finance. But here’s the deal: we’re moving beyond traditional AI systems […]

What is a Data Lake? Imagine you’re at the edge of a vast lake—calm, seemingly infinite, filled with water from rivers, streams, and even rainfall. Now, replace the water with data. That’s essentially what a data lake is—a massive storage repository where you can dump all kinds of data, whether it’s structured, semi-structured, or unstructured.

You’ve probably heard the phrase, “Data is the new oil.” But just like oil, raw data by itself isn’t valuable until it’s refined and put to use. In today’s business landscape, managing data efficiently has become as crucial as managing financial assets. According to a recent study, 90% of the world’s data has been created

“The world of gaming isn’t just evolving – it’s learning.”This might surprise you, but the way your favorite games anticipate your moves, create immersive worlds, or develop smarter, almost human-like opponents, is all thanks to deep learning. Take the infamous OpenAI’s Dota 2 bot, for instance. It didn’t just memorize strategies – it learned them

Let’s start with a simple truth: in the world of AI and machine learning, data is everything. But raw data alone is not enough—it’s the accuracy of annotated data that determines whether your machine learning model will be a hit or a flop. Here’s the deal: Data annotation is the backbone of every successful AI

“In a world drowning in data, how do you choose the right lifeboat?” Data is the currency of today’s digital economy, and the tools you choose to process it can make or break your data strategy. Apache Hadoop and Apache Spark are two of the most talked-about players in the big data arena. But why

Imagine you’re tasked with managing a fleet of delivery trucks. You need to figure out the most efficient way to get packages to multiple destinations, with constraints like time windows and fuel limits. The question isn’t just how to minimize costs—it’s how to balance dozens of factors and still arrive at the best possible solution.

You might not realize it, but every day, whether it’s scheduling flights, optimizing supply chains, or tuning machine learning models, deterministic optimization algorithms are silently at work, ensuring we get the best outcomes. They don’t rely on chance; they deliver precision—every single time. Hook: The Relevance of Deterministic Optimization Algorithms Here’s the deal: certainty is

What is Distributed Optimization? Let’s start with the basics, and don’t worry, I’ll keep it engaging! Optimization, at its heart, is about finding the best possible solution to a problem, given certain constraints. It’s something we all do in our daily lives—whether it’s optimizing our time, budget, or even picking the fastest route home. Now,

What are Hyperparameters? Let’s start with a simple analogy: Imagine you’re baking a cake. You can choose how much sugar, flour, and butter to put in. The recipe gives you a range, but you decide the exact quantities based on your taste. In machine learning, hyperparameters are like these ingredients—they’re the knobs you adjust before