Post1: Chapter: 5
- What is an artificial neural network and for what types of problems can it be used?
- Compare artificial and biological neural networks. What aspects of biological networks are not mimicked by artificial ones? What aspects are similar?
- What are the most common ANN architectures? For what types of problems can they be used?
- 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?
- List and briefly explain different learning paradigms/methods in AI.
- What is representation learning, and how does it relate to machine learning and deep learning?
- List and briefly describe the most commonly used ANN activation functions.
- 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 Watson and its success on Jeopardy!, cognitive computing and cognitive analytics are now part of many real-world intelligent systems. In this exercise, identify at least three application cases where cognitive computing was used to solve complex real-world problems. Summarize your findings in a professionally organized report.
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