Assuming the trends described in the previous question's figure are accurate (and hoping you got the axis labels right), which of the following are true? (Check all that apply.)

Increasing the size of a neural network generally does not hurt an algorithm's performance, and it may help significantly.

Increasing the training set size generally does not hurt an algorithm's performance, and it may help significantly.

Decreasing the training set size generally does not hurt an algorithm's performance, and it may help significantly.

Decreasing the size of a neural network generally does not hurt an algorithm's performance, and it may help significantly.

Given arrays a = np.random.randn(3, 3) and b = np.random.randn(2, 1), what happens when computing c = a + b?

The computation cannot happen because it is not possible to broadcast more than one dimension.

Given a = np.random.randn(1, 3) and b = np.random.randn(3, 3), what will be the shape of c = a * b?

The computation cannot happen because it is not possible to broadcast more than one dimension.

The computation cannot happen because the sizes don't match.

What is the result of np.dot(a, b) for a.shape = (12288, 150) and b.shape = (150, 45)?

The computation cannot happen because the sizes don't match.

Which of the following are true? (Check all that apply.)

a^[2] denotes the activation vector of the second layer.

w^[4]_3 is the column vector of parameters of the fourth layer and third neuron.

a_3^[2] denotes the activation vector of the second layer for the third example.

a^ denotes the activation vector of the second layer for the third example.

w^[4]_3 is the row vector of parameters of the fourth layer and third neuron.

Which of the following is a correct vectorized implementation of forward propagation for layer 2?

Z^[2] = W^[2] A^[1] + b^[2]; A^[2] = g^[2](Z^[2])

Z^[2] = W^[2] A^[1] + b^[2]; A^[2] = g(Z^[2])

Z^[1] = W^[1] X + b^[1]; A^[1] = g^[1](Z^[1])

Z^[2] = W^[2] X + b^[2]; A^[2] = g^[2](Z^[2])

Suppose you have built a neural network. You decide to initialize the weights and biases to be zero. Which of the following statements is true?

Each neuron in the first hidden layer will perform the same computation. So even after multiple iterations of gradient descent, each neuron in the layer will be computing the same thing as other neurons.

Each neuron in the first hidden layer will perform the same computation in the first iteration. But after one iteration they will learn to compute different things because we have 'broken symmetry'.

The first hidden layer's neurons will perform different computations even in the first iteration.

Each neuron in the first hidden layer will compute the same thing, but neurons in different layers will compute different things, thus breaking symmetry.

You have built a network using the tanh activation for all the hidden units. You initialize the weights to relatively large values, using np.random.randn(...) * 1000. What will happen?

This will cause the inputs of the tanh to also be very large, thus causing gradients to be close to zero. The optimization algorithm will thus become slow.

This will cause the units to be 'highly activated' and thus speed up learning.

Gradient descent is not affected by whether the weights are large or small.

This will cause gradients to be large. You'll need a small alpha to prevent divergence, slowing learning.

Which of the following are 'parameters' of a neural network? (Check all that apply.)

L the number of layers of the neural network.

g^[l] the activation functions.

Which of the following statements is true?

The deeper layers of a neural network are typically computing more complex features of the input than the earlier layers.

The earlier layers of a neural network are typically computing more complex features of the input than the deeper layers.

What is the dimension of W^[l], the weight matrix associated with layer l?

W^[l] has shape (n^[l], n^[l−1])

W^[l] has shape (n^[l+1], n^[l])

W^[l] has shape (n^[l], n^[l+1])

W^[l] has shape (n^[l−1], n^[l])

In a personal experiment, an M.L. student decides to not use a test set, only train-dev sets. Which of the following is true?

He might be overfitting to the dev set.

He won't be able to measure the bias of the model.

Not having a test set is unacceptable under any circumstance.

He won't be able to measure the variance of the model.

If your Neural Network model seems to have high variance, what would be promising things to try? (Check all that apply.)

Increase the number of units in each hidden layer.

Make the Neural Network deeper.

You are building a classifier and your training error is 19% and dev error is 21%. What are promising things to try? (Check all that apply.)

Increase the regularization parameter lambda.

What is weight decay?

A regularization technique (such as L2 regularization) that results in gradient descent shrinking the weights on every iteration.

The process of gradually decreasing the learning rate during training.

A technique to avoid vanishing gradient.

Gradual corruption of weights in noisy data.

What happens when you increase the regularization hyperparameter lambda?

Weights are pushed toward becoming smaller (closer to 0).

Doubling lambda should roughly result in doubling the weights.

Gradient descent takes bigger steps.

Weights are pushed toward becoming bigger.

With the inverted dropout technique, at test time:

You do not apply dropout (do not randomly eliminate units) and do not keep the 1/keep_prob factor in the calculations used in training.

You apply dropout and keep the 1/keep_prob factor.

You do not apply dropout but keep the 1/keep_prob factor.

You apply dropout and do not keep the 1/keep_prob factor.

During training a deep neural network that uses tanh, gradients can become close to zero. What helps with this vanishing gradient problem?

Use a larger regularization parameter.

Increase number of training cycles.

Which of the following actions increase the regularization of a model? (Check all that apply.)

Increase the value of the hyperparameter lambda.

Decrease the value of keep_prob in dropout.

Increase the value of keep_prob in dropout.

Decrease the value of the hyperparameter lambda.

Why do we normalize the inputs x?

It makes the cost function faster to optimize.

It makes the parameter initialization faster.

It makes it easier to visualize the data.

Normalization is the same as regularization.

Which of these statements about mini-batch gradient descent do you agree with?

Training one epoch using mini-batch gradient descent is faster than training one epoch using batch gradient descent.

One iteration of mini-batch GD is faster than batch GD.

You should implement mini-batch GD without an explicit loop over batches.

Which of the following is true about batch gradient descent?

It is the same as mini-batch gradient descent when the mini-batch size is the same as the size of the training set.

It has as many mini-batches as examples.

It is the same as stochastic GD but without random elements.

Which of the following statements about Adam is False?

Adam should be used with batch gradient computations, not with mini-batches.

We usually use default values for β₁, β₂, ε.

The learning rate α in Adam usually needs tuning.

Adam combines the advantages of RMSProp and momentum.

Which of the following are true about hyperparameter search?

Choosing random values for the hyperparameters is convenient since we might not know in advance which hyperparameters are more important for the problem at hand.

When using random values for the hyperparameters they must be always uniformly distributed.

Choosing values in a grid is always better because it is more ordered.

When sampling from a grid, the number of values for each hyperparameter is larger than random.

Knowing that the hyperparameter α should be in the range of 0.001 and 1.0, what is the recommended way to sample a value for α?

r = -3 * np.random.rand(); alpha = 10**r

r = 4 * np.random.rand(); alpha = 10**r

r = np.random.rand(); alpha = 0.001 + r * 0.999

r = -5 * np.random.rand(); alpha = 10**r

Which of the following are true about batch normalization?

One intuition behind why batch normalization works is that it helps reduce the internal covariance.

The parameter ε is used to accelerate convergence.

There is a global value of γ and β used for all hidden layers.

The parameters β and γ can't be trained using Adam or RMSProp.

AI and Machine Learning Quiz

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MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Which of the following are some aspects in which AI has transformed business?

Web searching and advertisement.

AI has not been able to transform businesses.

Eliminating the need for health care services.

Creating an AI-powered society.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Which of these are reasons for Deep Learning recently taking off? (Check the three options that apply.)

We have access to a lot more data.

We have access to a lot more computational power.

Deep learning has resulted in significant improvements in important applications such as online advertising, speech recognition, and image recognition.

Neural Networks are a brand new field.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Recall this diagram of iterating over different ML ideas. Which of the statements below are true? (Check all that apply.)

Being able to try out ideas quickly allows deep learning engineers to iterate more quickly.

Faster computation can help speed up how long a team takes to iterate to a good idea.

Recent progress in deep learning algorithms has allowed us to train good models faster (even without changing the CPU/GPU hardware).

It is faster to train on a big dataset than a small dataset.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Neural networks are good at figuring out functions relating an input x to an output y given enough examples. True/False?

True

False

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Features of animals, such as weight, height, and color, are used for classification between cats, dogs, or others. This is an example of "structured" data, because they are represented as arrays in a computer. True/False?

True

False

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

A dataset is composed of age and weight data for several people. This dataset is an example of "structured" data because it is represented as an array in a computer. True/False?

True

False

MULTIPLE SELECT QUESTION

30 sec • 1 pt

Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? (Check all that apply.)

It is applicable when the input/output is a sequence (e.g., a sequence of words).

It can be trained as a supervised learning problem.

RNNs represent the recurrent process of Idea->Code->Experiment->Idea->....

It is strictly more powerful than a Convolutional Neural Network (CNN).

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AI and Machine Learning Quiz

Which of the following are some aspects in which AI has transformed business?

Which of these are reasons for Deep Learning recently taking off? (Check the three options that apply.)

Recall this diagram of iterating over different ML ideas. Which of the statements below are true? (Check all that apply.)

Neural networks are good at figuring out functions relating an input x to an output y given enough examples. True/False?

Features of animals, such as weight, height, and color, are used for classification between cats, dogs, or others. This is an example of "structured" data, because they are represented as arrays in a computer. True/False?

A dataset is composed of age and weight data for several people. This dataset is an example of "structured" data because it is represented as an array in a computer. True/False?

Why is an RNN (Recurrent Neural Network) used for machine translation, say translating English to French? (Check all that apply.)

From the given diagram, we can deduce that Large NN models are always better than traditional learning algorithms. True/False?

Assuming the trends described in the previous question's figure are accurate (and hoping you got the axis labels right), which of the following are true? (Check all that apply.)

In logistic regression, which of the following best expresses what we want ŷ to tell us?

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