Python for Deep Learning - Build Neural Networks in Python - How do Artificial Neural Networks Work?

The video tutorial reviews neural networks, explaining their structure with input and output layers. It contrasts machine learning models, which lack hidden layers, with deep learning models that include them for improved accuracy. An example of calculating house prices is used to illustrate these concepts. The tutorial also details the workflow of an artificial neural network, highlighting the role of activation functions and the importance of hidden layers in enhancing model performance.

What is the primary function of the input layer in a neural network?

• To connect neurons in the hidden layer

• To process the final output

• To apply activation functions

• To hold the values of each attribute from the data

How does the addition of a hidden layer improve a neural network?

• By simplifying the output layer

• By increasing the complexity of the input layer

• By providing flexibility and improving accuracy

• By reducing the number of neurons

What role do weights play in a neural network?

• They simplify the network structure

• They adjust the importance of inputs in the network

• They are used to calculate the final output directly

• They determine the number of neurons in the output layer

Which function is used to map the summation value to another value in the workflow of an artificial neural network?

• Sigmoid function

• ReLU function

• Tanh function

• Linear function

What is the final step in the workflow of an artificial neural network?

• Calculating the summation of inputs

• Applying the activation function

• Repeating the process until the output layer is reached

• Forwarding the output to the next neuron