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

The video tutorial explains how to calculate the total number of weights or parameters in a deep neural network. It covers the step-by-step process of determining weights for each layer, including the input, hidden, and output layers. The tutorial also discusses the implications of having a large number of weights, such as increased model complexity and the risk of overfitting, especially with limited training data. The importance of considering the number of parameters when designing a neural network architecture is emphasized.

What new insight or understanding did you gain from this video?