Deep Learning - Crash Course 2023 - Regression Problem

The video tutorial covers solving a regression problem using the Boston Housing dataset. It explains data preprocessing with scaling, building a neural network with specific layers and activation functions, and training the model using K-fold validation. The tutorial also discusses evaluating model performance using mean absolute error and addresses overfitting issues. Finally, it summarizes the application of deep learning models to regression problems.

What is the primary goal when using the Boston Housing dataset in this tutorial?

• To determine the crime rate in Boston

• To predict the median prices of homes

• To classify houses into different categories

• To analyze the local property tax rate

Why is it important to scale the data before training the model?

• To ensure the data has zero mean and unit variance

• To reduce the size of the dataset

• To make the data more complex

• To increase the number of features

What activation function is used in the hidden layers of the neural network?

• Sigmoid

• Tanh

• ReLU

• Linear

Why is no activation function used in the output layer of the neural network?

• To allow the output to be a continuous value

• To reduce overfitting

• To limit the output to binary values

• To make the model faster

What is the purpose of using K-fold cross-validation?

• To ensure the model is evaluated on different subsets of data

• To reduce the number of training epochs

• To increase the size of the dataset

• To simplify the model architecture

How many folds are typically used in K-fold cross-validation?

• 4 or 5

• 2 or 3

• 6 or 7

• 8 or 9

What does a mean absolute error (MAE) of 0.9 indicate?

• The predictions are off by $9,000 on average

• The predictions are off by $900 on average

• The predictions are off by $90,000 on average

• The predictions are off by $90 on average

What is the significance of the epoch number in training a neural network?

• It specifies the number of neurons in each layer

• It determines the number of layers in the network

• It controls the learning rate of the model

• It indicates the number of times the entire dataset is passed through the network

What does the plot of validation loss versus epochs help to identify?

• The optimal number of features

• The best learning rate

• The point where the model starts overfitting

• The ideal batch size

At which epoch did the model achieve maximum accuracy on the validation set?

• Epoch 30

• Epoch 41

• Epoch 50

• Epoch 60