Deep Learning - Deep Neural Network for Beginners Using Python - Final Project Part 4

The video tutorial covers the development of a neural network prediction function, focusing on the use of weights and features. It explains the process of obtaining activations and outputs, and how to handle them using one hot encoding. The tutorial also includes writing an accuracy function and a comprehensive neural network function with parameters like epochs and learning rate. Finally, it discusses the training and validation process, emphasizing the importance of early stopping to prevent overfitting.

What is the purpose of appending a bias to the input features in a neural network?

• To increase the number of features

• To improve the accuracy of predictions

• To account for the intercept in the model

• To reduce the complexity of the model

In the feedforward process, what determines the final output of the neural network?

• The average of all neuron outputs

• The sum of all activations

• The first neuron in the last layer

• The neuron with the highest activation value

Why is one-hot encoding used for predicted labels in neural networks?

• To reduce the number of output neurons

• To ensure each class is represented as a unique vector

• To match the format of the input features

• To simplify the model

What is the role of the accuracy function in evaluating a neural network?

• To measure the model's performance

• To update the weights

• To calculate the loss

• To adjust the learning rate

Which parameter is NOT typically received by the neural network function?

• Training data

• Validation data

• Learning rate

• Number of hidden layers

What is the purpose of using validation data during training?

• To increase the training speed

• To test the model's performance on unseen data

• To adjust the number of epochs

• To reduce the size of the training set

What is the default learning rate set in the neural network function?

• 0.01

• 0.1

• 0.2

• 0.15

How often are results printed during the training process?

• After every epoch

• After every 20 epochs

• After every 10 epochs

• After every 50 epochs

What does early stopping help prevent in the training of neural networks?

• Data leakage

• Underfitting

• Gradient vanishing

• Overfitting

When should training be stopped according to the early stopping technique?

• When training accuracy is increasing and validation accuracy is decreasing

• When both training and validation accuracy are increasing

• When validation accuracy is increasing

• When training accuracy is decreasing