The Chain Rule of Calculus: The Backbone of Deep Learning Backpropagation

Deep learning has witnessed remarkable growth and transformation in recent years, enabling machines to tackle complex tasks, such as image recognition, natural language processing, and autonomous driving. One of the key components underpinning the success of neural networks is backpropagation, a crucial technique for training deep models. At the heart of backpropagation lies the chain rule of calculus, which allows gradients to be efficiently computed and propagated throughout the network. In this article, we will delve into the chain rule and how it serves as the backbone of deep neural networks, enabling them to learn and generalize from data effectively. <a href="https://medium.com/@ppuneeth73/the-chain-rule-of-calculus-the-backbone-of-deep-learning-backpropagation-9d35affc05e7">Visit Now</a>