Understanding Deep Learning Concepts

Which of the following best describes Artificial Intelligence (AI)?

• Learning patterns from data

• A system that can mimic human-like intelligence

• Creating deep neural networks

• Data clustering and classification

Which type of learning is used in Deep Learning (DL)?

• Rule-based programming

• Shallow learning

• Multi-layered neural networks

• Single-layer networks

How does Machine Learning (ML) differ from Deep Learning (DL)?

• ML uses large datasets; DL uses small datasets

• ML uses neural networks; DL uses traditional algorithms

• ML requires feature extraction; DL does not

• DL always uses decision trees

What is one key advantage of Deep Learning over traditional Machine Learning?

• Faster computation for small datasets

• Ability to handle large amounts of unstructured data

• Use of manual feature extraction

• Low computational power requirement

Why has Deep Learning become more relevant in recent years?

• The availability of large datasets and better hardware

• The reduction in the need for labeled data

• The development of shallow algorithms

• The decline in data complexity

What is a primary advantage of neural networks in Deep Learning?

• They always require small datasets

• They can model non-linear relationships in data

• They avoid the need for computation

• They do not require training

What is backpropagation used for in neural networks?

• To store data

• To optimize the weights by reducing the error

• To visualize the data

• To classify the data into clusters

What do hidden layers in a neural network help achieve?

• Perform simple calculations

• Extract and learn hierarchical features

• Reduce computation time

• Perform data collection

Why is Deep Learning preferred for image and speech recognition tasks?

• It requires less data

• It can automatically extract features from raw data

• It uses linear models

• It does not require training