Deep Learning - Deep Neural Network for Beginners Using Python - Updating Weights

The video tutorial discusses the process of updating weights in a deep neural network using gradient descent. It explains the error function and how to update specific weights, particularly focusing on the challenges of updating weights in initial layers using errors from the last layer. The tutorial emphasizes the importance of the learning rate and partial derivatives in this process. The video concludes by introducing the next lecture, which will cover taking derivatives of weights in initial layers with respect to errors in the last layer.

Discuss the challenges of updating weights in the initial layers of a deep neural network.

What is the relationship between the error function and the weight updates in a neural network?