An undergraduate introduction to quantum computing with detailed explanation on fundamental concepts building towards a basic understanding of Quantum Mechanics.

In this post, we peel back the abstraction layers of modern deep learning frameworks by performing a rigorous mathematical deep dive into a 2-Layer Fully Connected Neural Network. We will trace the input dimensions as they transform through the forward pass, and manually derive the gradients for the backward pass. By the end of this tutorial, you won't just know that it works—you'll understand exactly how the numbers flow.