Fundamentals of Neural Networks - Forward Propagation

The video tutorial explains the architecture of a neural network using a housing dataset. It covers the concept of fully connected layers, weights, and forward propagation. The tutorial introduces the linear combination of inputs and weights, followed by the selection of an activation function, specifically the Radu function. The Radu function is defined and graphed, showing its application in predicting housing prices. The video concludes with a discussion on forward propagation and the orientation of neural network graphs.

What is the significance of using a non-linear activation function in neural networks?

How does the Radu activation function work, and why is it chosen for this architecture?

Discuss the importance of ensuring that the architecture of a neural network fits the assumptions of the data.