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.

How many weights does the first neuron have?

• 2

• 3

• 4

• 5

What is the total number of weights for layer one if there are 5 neurons?

• 20

• 24

• 30

• 15

How many weights does a neuron in layer two receive?

• 5

• 6

• 4

• 3

What is the total number of parameters for the deep neural network discussed?

• 54

• 50

• 60

• 40

Why is it important to consider the number of weights in a neural network?

• To avoid overfitting

• To increase complexity

• To reduce training time

• To simplify the model