Sean describes gradient descent through an analogy of finding the deepest point in a body of water, emphasizing its greedy approach to optimization. He highlights the importance of loss functions in deep learning and the challenge of ensuring that the model reaches an optimal point. The conversation then shifts to practical applications, specifically focusing on workflows for detecting credit card fraud using viable datasets.
