Fundamentals of Neural Networks - Backward Propagation

The video tutorial covers the basics of neural networks, focusing on the flow of information from input to output layers. It introduces backward propagation, explaining the gradient descent algorithm used for optimization. The tutorial discusses the loss function, particularly mean square error, and draws an analogy to ordinary least squares (OLS) in linear regression. It details the steps of gradient descent, emphasizing the importance of the learning rate (ETA) and the challenges of exploding and vanishing gradients. The tutorial aims to provide a foundational understanding of these concepts for effective neural network training.

What is the purpose of backward propagation in neural networks?

Explain the concept of the loss function in the context of backward propagation.

How does the mean square error relate to the loss function?

How does backward propagation relate to the conventional sense of statistical learning?

What steps are involved in optimizing the loss function during backward propagation?

What is the significance of the learning rate (ETA) in gradient descent?

Describe the potential issues that can arise from having a very small or very large learning rate.