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 a single neuron in a fully connected layer have if it receives four inputs?

• Two

• Five

• Four

• Three

If a layer has 5 neurons and each neuron has 4 weights, what is the total number of weights for that layer?

• 15

• 20

• 10

• 25

How many weights does a neuron in the second layer have if it receives inputs from 5 neurons in the previous layer and one additional input?

• Six

• Five

• Four

• Seven

What is the total number of weights for the output layer if it has 2 neurons, each receiving 5 inputs?

• 20

• 15

• 5

• 10

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

• It affects the speed of the network.

• It changes the type of data the network can process.

• It determines the color of the network.

• It influences the complexity and risk of overfitting.