Deep Learning from Scratch: The Math Behind AI

In this episode of Echo Verse AI Originals, we dive into the fundamentals of deep learning with a back-to-basics approach. Inspired by Seth Weidman’s Deep Learning from Scratch, we explore the mathematical foundations behind neural networks, from derivatives to computational graphs. We break down the process of building AI models from first principles, demystifying complex architectures like convolutional and recurrent neural networks. Whether you're an AI enthusiast or a developer looking to deepen your understanding, this episode offers insights into how deep learning works at its core—without relying on pre-built libraries.