Welcome to the latest episode of the AI Concepts Podcast, hosted by Shay, where we continue our exploration of deep learning. In this installment, we delve into the mechanics of backpropagation, the algorithm that empowers neural networks to optimize and learn from their mistakes.

We start by revisiting fundamental concepts of neural networks, exploring how data flows forward from input to output. But the real focus is on what happens when predictions aren’t perfect—a journey into understanding errors and their corrections through the backpropagation process.

Listen as we break down each step: from calculating errors, sending them backward through the network, to determining how each weight impacts the outcome. Discover how backpropagation acts as a detective, tracing errors back to their roots, providing the optimizer with crucial gradient information to improve network performance.

This episode sets the stage for our next conversation about the optimization technique of gradient descent, crucial for turning the insights obtained from backpropagation into actionable improvements in model accuracy. Stay tuned for a practical, accessible guide to mastering these essential deep learning components.