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

The video tutorial explains the concept of parameters or weights in a deep neural network. It uses a simple example with three features and two layers to illustrate the structure of a neural network. The tutorial details the number of neurons in each layer and emphasizes the importance of counting the total number of weights, which are the parameters of the network. A hint is provided to consider an extra edge when calculating parameters, with a promise to reveal the solution in the next video.

What is the primary focus of the initial discussion in the video?

• The applications of neural networks

• The concept of parameters or weights in a neural network

• The history of neural networks

• The limitations of neural networks

How many features are used in the example network discussed?

• Five

• Four

• Three

• Two

How many neurons are there in the first layer of the network?

• Three

• Two

• Five

• Four

What is the total number of neurons in the output layer?

• Four

• One

• Two

• Three

What should be considered when calculating the total number of parameters?

• The number of layers

• The activation function used

• The number of features

• An extra edge in the network