In this episode of the AI Concepts Podcast, we dive into the fascinating world of gradient descent. Building on the foundation laid in our discussion of backpropagation, we explore how gradient descent serves as a pivotal optimization algorithm in deep learning. Discover how it minimizes loss functions by adjusting model parameters and learn why selecting the right learning rate is crucial. Join us as we differentiate between batch, stochastic, and mini-batch gradient descents, setting the stage for our next episode on advanced optimization techniques.