What is the goal of supervised learning?

• Find an underlying structure of the dataset without any labels.

• Predict the labels.

• Predict the features.

• Find the target

What is deep learning?

• Deep learning is machine learning that involves deep neural networks.

• Deep learning is another name for artificial intelligence.

• None of the above are correct

When is a standard machine learning algorithm usually a better choice than using deep learning to get the job done?

• When working with small data sets.

• When the data is steady over time.

• When working with large data sets.

• None of the above are correct.

What is a Turing test?

• It tests and cleans the dataset.

• It tests a machine's ability to exhibit intelligent behavior.

• It tests the dataset.

• It tests the images

What are some of the different milestones in deep learning history?

• Geoffrey Hinton’s work, AlexNet, and TensorFlow

• Deep Blue defeats a world champion chess player, and AlexNet is created.

• Deep Blue defeats a world champion chess player and TensorFlow is released

• Deep Blue defeats a world champion chess player, and Keras is released.

What is artificial intelligence?

• A subset of deep learning.

• Any program that can sense, reason, act, and adapt.

• A subset of machine learning

• None of the above.

What are two spaces within AI that are going through drastic growth and innovation?

• Computer vision and natural language processing.

• Computer vision and deep learning.

• Language processing and deep learning.

• Deep learning and machine learning.

Why did AI flourish so much in the last years?

• Access to hardware for cleaning data

• Data storage in the cloud is much more expensive

• Faster and inexpensive computers and data storage

• Stylish designed computers

How does Alexa use artificial intelligence?

• Recognizes faces and pictures.

• Recognizes our voice and answers questions.

• Suggests who a person on a photo is.

• None of the above answers are correct.

What are the first two steps of a typical machine learning workflow?

• Problem statement and data cleaning.

• Problem statement and data collection.

• Data collection and data transformation.

• None of the above answers is correct.