New England College of Business and Finance Computer Science Discussion

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Computer Science

New England College of Business and Finance

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Week 3 Assignment

Complete the following assignment in one MS word document:

Chapter 5 –discussion question #1-4 (roughly 100-200 words each) & exercise 6 & internet exercise #7 (go to neuroshell.com click on the examples and look at the current examples. The Gee Whiz example is no longer on the page. I did find mention of it here: https://nstsupport.wardsystemsgroup.com/support/ge... that might help you respond to the questions here. )

Chapter 6– discussion question #1-5 (roughly 100-200 words each) & exercise 4

When submitting work, be sure to include an APA cover page and include at least two APA formatted references (and APA in-text citations) to support the work this week.

All work must be original (not copied from any source). It is checked via SafeAssign. I ensure that the SafeAssign is not just finding matches for the questions and bibliography.


Questions


Chapter 5

Questions for Discussion

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.

Exercise6

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

Internet exercise 7

7. 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.

Chapter 6

Questions for Discussion

1. 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 activation functions.

5. What is MLP, and how does it work? Explain the function

Chapter4

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 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 your findings in a professionally organized report

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Explanation & Answer

Attached. Please let me know if you have any questions or need revisions.

COMPUTER SCIENCE

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Computer Science
Name
[Institutional Affiliation(s)]
Instructor
Date submitted

COMPUTER SCIENCE

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Chapter 5

Questions for Discussion
1. What is an artificial neural network and for what types of problems can it be used? An
artificial neural network is a computer system that is modelled after the human brain and utilizes
the use of neurons to help the system to learn on its own as more data is accumulated by the
system. Just like a brain, artificial neural networks have a network of interconnected artificial
neurons which pass data from one neuron to the other. The input of one neuron is analyzed and
passed over to the next neuron. Hence, the output of a neuron is the input of the next neuron.
Artificial neural networks thrive on having data and massive volumes of data. This makes them
useful in solving some of the problems that might be hard to solve by humans. Problems solved
include: classifications, pattern recognitions, optimization, prediction and filtering.
2. Compare artificial and biological neural networks. What aspects of biological networks
are not mimicked by artificial ones? What aspects are similar? Artificial neural networks
were inspired by the human brain. The human brain is estimated to have billions of neurons. And
not a billion or 2 billion but an approximation of 86 billion neurons. The brain receives data from
the various input channels. These include: eyes, for visual data, ears for sensory data, nose for
smelling, mouth for taste. All these components work together to provide the brain with data
which it processes, interprets and provides feedback in just a matter of seconds. Artificial neural
networks similarly are composed of neurons. However, humans have to provide the systems with
data so that it can learn and recognize patterns, they are similar in that they both depend on data
and are composed of neurons. They are different in that the brain has more neurons and is fault
tolerant. Additionally, the brain is fast. Artificial neural networks are a bit slow compared to the
brain and small in siz...


Anonymous
I was having a hard time with this subject, and this was a great help.

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