Reinforcement Learning and Deep RL Python Theory and Projects - DNN Architecture Exercise Solution

The video tutorial explains how to calculate the total number of weights in a neural network. It covers the process for each layer, including the input, hidden, and output layers. The tutorial emphasizes the importance of understanding the number of weights as it relates to model complexity and the risk of overfitting, especially with limited training data.

How many weights does the first neuron have?

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

How many weights does a neuron in layer two receive?

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

Why is it important to consider the number of weights when designing a deep neural network?