Deep Learning CNN Convolutional Neural Networks with Python - DNN Architecture

The video tutorial introduces the concept of neurons in neural networks, explaining how they process inputs through weighted sums and activation functions. It covers the architecture of neural networks, including input, hidden, and output layers, and discusses the importance of activation functions in creating complex networks. The tutorial also explains the role of bias terms and the computation of weights in hidden layers. Finally, it defines deep neural networks and outlines the parameters involved in their construction.

What is the role of the activation function in a neuron?

Explain the architecture of a neural network with respect to input and output layers.

What is the significance of the bias term in a neural network?

Describe the difference between hidden layers and input/output layers in a neural network.

How do neurons in a layer compute their outputs based on inputs and weights?

In a binary classification problem, how do the output neurons determine the class of an input?

What defines a neural network as 'deep'?