Data Science and Machine Learning (Theory and Projects) A to Z - DNN and Deep Learning Basics: DNN Gradient Descent

The video tutorial explains the importance of selecting the right parameters for a neural network and how a well-designed architecture can be ineffective without optimal parameters. It introduces the concept of a loss function as a measure of network performance and explains how gradient descent is used to optimize parameters. The tutorial covers the computation of gradients using automatic differentiation, particularly in Pytorch, and discusses the implementation of gradient descent, including considerations for the learning rate.

What challenges might arise when using automatic differentiation?

How does gradient descent help in optimizing the parameters of a neural network?

What factors should be considered when choosing the step size for gradient descent?