Grant discusses the application of calculus in machine learning, specifically the back propagation algorithm and its calculations. Back propagation is essential for optimizing neural networks through detailed calculus applications.

What's actually happening to a neural network as it learns? Grant discusses back propagation, a key algorithm behind how neural networks learn, using the example of recognizing handwritten numbers.

Andrej presents a comprehensive lecture on training deep neural networks, explaining the creation and training of a neural network using his library, micrograd, and comparing it to PyTorch. Understanding and training neural networks requires mastering concepts like backpropagation, loss functions, and gradient descent. Taught by Andrej Kaparthy, a founding member of OpenAI and a ex-senior director of AI at Tesla.