Deep Learning and Reinforcement Learning Week 2 All Quiz

Practice: Back Propagation, Activation Functions 

1. Select the method or methods that best help you find the same results as using matrix linear algebra to solve the equation θ=(XTX)−1XTy

A) Use stochastic gradient descent
B) Use scikit-learn to build a linear regression model
C) Train a neural network model
D) All the above

ANSWER= (D) All the above

 

2. (True/False) Neurons can be used as logic gates

A) True
B) False

ANSWER= (A) True

 

3. (True/False) The feed-forward computation of a neural network can be thought of as matrix calculations and activation functions.

A) True
B) False

ANSWER= (A) True

Practice: Keras Library 

1. Building a Neural Network with the Sequential API in Keras implies that each layer

A) can connect only to subsequent layers.
B) after the first layer must be fully-connected.
C) can connect to only the previous and next layers.
D) can be connected to at most two subsequent layers.

ANSWER= (C) can connect to only the previous and next layers.

 

2. An epoch in estimating a Deep Learning model refers to

A) subsamples that make up the training data.
B) iterations required to achieve convergence.
C) computational complexity of the estimating procedure.
D) the number of times the entire input data set is used by the model.

ANSWER= (D) the number of times the entire input data set is used by the model.

 

3. An advantage of the Sigmoid activation function over the step activation function is:

A) the ability to generate nonlinear outcomes.
B) sharper changes as the loss becomes positive.
C) improved backpropagation due to nonzero gradients.
D) significantly different gradients for very high values.

ANSWER= (C) improved backpropagation due to nonzero gradients.

Week 2 Final Quiz  

1. The backpropagation algorithm updates which of the following?

A) The parameters only.
B) The losses only.
C) The parameters and activations.
D) The activations only.

ANSWER= (A) The parameters only.

 

2. What of the following about the activation functions is true?

A) They add non-linearity into the model, allowing the model to learn complex pattern.
B) They are optimization algorithms that update values of the model parameters.
C) They evaluate how well the model has performed on the training data.
D) They tell us about how computationally expensive a neural network is.

ANSWER= (A) They add non-linearity into the model, allowing the model to learn complex pattern.

 

3. What is true regarding the backpropagation rule?

A) It prevents overfitting
B) The actual output is determined by computing the output of neurons in each hidden layer
C) It can be used to update the hyperparameters of a neural network
D) It is a feed forward neural network.

ANSWER= (B) The actual output is determined by computing the output of neurons in each hidden layer

 

4. Which option correctly lists the steps to build a linear regression model using Keras?
1. Use `fit()` and specify the number of epochs to train the model for.
2. Create a Sequential model with the relevant layers.
3. Normalize the features with ` layers.Normalization()` and apply `adapt()`.
4. Compile using `model.compile()` with specified optimizer and loss.

A) 3, 2, 4, 1
B) 3, 1, 2, 4
C) 3, 2, 1, 4
D) 2, 4, 3, 1

ANSWER= (A) 3, 2, 4, 1

 

5. (True/False) Keras provides one approach to build a model: by defining a Sequential model.

A) True
B) False

ANSWER= (B) False