Adarsh Kesharwani

Introduction: Why Accents Matter in AI Have you ever noticed how Siri struggles with strong accents? Or how Zoom’s auto-captions mishear non-native speakers? Behind the scenes, accent embedding models are working to solve these problems. In this blog, I’ll break down how I built a system that converts raw speech into numerical “accent fingerprints” — and explain every component. 1. Audio Preprocessing: The Quality Control Pipeline Before analyzing accents, we need clean, standardized audio. This preproce...

1. Introduction to PINNs Physics-Informed Neural Networks (PINNs) are a powerful blend of deep learning and physics. They train neural networks to not just fit data, but also respect the underlying physical laws governing the system. In this blog, we’ll explore how PINNs work by modeling a hot cup of coffee cooling down over time — a simple, real-world example that follows Newton’s Law of Cooling. 2. Traditional Neural Networks vs. PINNs Standard Neural Networks (Pure Data-Driven) Input → Output Mapping:...

A Step-by-Step Guide DreamBooth And LoRA: A Quick Overview Think of DreamBooth as a way to teach an AI model something special — like your favorite art style, a unique character, or even a specific object. You only need a few images to fine-tune the model, and it starts generating outputs tailored to what you taught it. This makes it perfect for adding a personal touch to AI-generated content. Now, let’s talk about LoRA (Low-Rank Adaptation). Training large AI models from scratch can be time-consuming an...