Amit Yadav

Machine learning is everywhere today—from recommending your next Netflix binge to driving cars. But what if I told you that one of the most exciting frontiers for ML is something we all love: games? That’s right, ML isn’t just transforming industries like healthcare and finance; it’s revolutionizing gaming too. Whether you’re a developer building the […]

Imagine this: You’re analyzing the results of a marketing campaign and you see an uptick in sales. But here’s the real question—did your campaign cause those sales, or is it just a coincidence? This is where understanding cause-and-effect relationships becomes crucial in data-driven decision-making. Whether it’s determining the effectiveness of a drug in healthcare, assessing

They say, “A picture is worth a thousand words.” But in the world of computers, an image is much more than that—it’s data, and lots of it. That’s where image processing steps in. Simply put, image processing is how machines analyze, manipulate, and understand visual information, much like how your brain interprets the world around

You know, it’s fascinating how deep neural networks (DNNs) have transformed industries that were once thought to be untouchable by technology—think autonomous vehicles steering themselves through traffic, or AI algorithms in healthcare identifying tumors faster than any doctor. Even in finance, DNNs are making markets more efficient, predicting trends, and driving decision-making like never before.

Let’s start with the basics. You’ve probably heard this saying before: “Less is more.” Well, that pretty much sums up quantization in deep learning. You see, deep learning models tend to be big. I mean, really big—millions, even billions, of parameters. And while that’s great for accuracy, it’s not so great for deploying these models

“The ability to see and interpret the world is one of the most remarkable aspects of human intelligence. Today, machines are rapidly catching up with our ability to detect, identify, and track objects—thanks to object detection models.” Imagine self-driving cars identifying pedestrians on the street or your smartphone detecting faces in real time. These technologies

Picture this: Deep learning is now everywhere, from your smartphone’s voice assistant to self-driving cars, but as these AI models grow more powerful, they also grow more demanding. Enter low-precision techniques, a game-changing trend in deep learning. Why? Because they can drastically reduce computational load without compromising much of your model’s performance. In the quest

Let’s start with a simple question: Why are we obsessed with making things smaller and faster? Think of the first mobile phones, which were huge, heavy bricks. Today, we carry supercomputers in our pockets. This shift isn’t just about making things physically smaller; it’s about efficiency. In AI and machine learning, the push for efficiency

You’ve probably heard the term “cognitive computing” thrown around in conversations about AI and tech, but what does it really mean? To break it down simply, cognitive computing refers to systems that mimic human thought processes. Imagine a machine that not only processes information but does so in a way that resembles how you and

“You cannot teach a man anything; you can only help him find it within himself.” — Galileo Galilei This quote beautifully captures the essence of the evolving relationship between humans and machines. In today’s world, technology has come a long way, but it’s not about replacing us—it’s about augmenting us, making our decisions smarter, faster,