Perceptrons are the foundational building blocks of neural networks. In this article, we explore how perceptrons work, their role in AI development, and their limitations. Understanding perceptrons is the first step toward mastering modern neural network technologies.

Gradient descent is a fundamental optimization algorithm in machine learning, driving techniques like neural networks. This article breaks down the concept, explains how it works, and explores key modifications like stochastic and mini-batch gradient descent. We also discuss challenges like choosing the right learning rate and avoiding local minima, with practical insights and Python examples.

Machine Learning powers everything from social media algorithms to self-driving cars, but what exactly is it? In this article, we break down the basics of Machine Learning in simple terms, explore how it differs from Artificial Intelligence, and explain key concepts like supervised, unsupervised, and reinforcement learning—without the technical jargon.

If you ever wanted to learn more about AI, you’ve likely encountered one of the biggest challenges of modern internet—Information pollution. Like every other trending topic, AI also suffers from it. While it is not hard to find information about AI, if you don’t have any experience or previous knowledge about the particular topic you want to learn, it can be difficult to identify bad explanations, poorly written tutorials, or outdated tech stacks.