1. What is an artificial neural network and for what types
of problems can it be used?
2. Compare artificial and biological neural networks. What
aspects of biological networks are not mimicked by artificial
ones? What aspects are similar?
3. What are the most common ANN architectures? For
what types of problems can they be used?
4. ANN can be used for both supervised and unsupervised
learning. Explain how they learn in a supervised mode
and in an unsupervised mode.
Go to Google Scholar (scholar.google.com). Conduct
a search to find two papers written in the last five years
that compare and contrast multiple machine-learning
methods for a given problem domain. Observe commonalities
and differences among their findings and
prepare a report to summarize your understanding.
Go to neuroshell.com. Look at Gee Whiz examples.
Comment on the feasibility of achieving the results
claimed by the developers of this neural network model.
What is deep learning? What can deep learning do that
traditional machine-learning methods cannot?
2. List and briefly explain different learning paradigms/
methods in AI.
3. What is representation learning, and how does it relate
to machine learning and deep learning?
4. List and briefly describe the most commonly used ANN
5. What is MLP, and how does it work? Explain the function
of summation and activation weights in MLP-type ANN.
Cognitive computing has become a popular term to define
and characterize the extent of the ability of machines/
computers to show “intelligent” behavior. Thanks to IBM
Chapter 6 • Deep Learning and Cognitive Computing 385
Watson and its success on Jeopardy!, cognitive computing
and cognitive analytics are now part of many realworld
intelligent systems. In this exercise, identify at least
three application cases where cognitive computing was
used to solve complex real-world problems. Summarize