AI and Machine Learning Quiz

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.

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.

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.

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

• 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?

• 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?

• True

• False

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).

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

• False

• True

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.

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

• P(y = 1 | x)

• σ(Wx)

• P(y = ŷ | x)

• σ(Wx + b)