Directions: Answer the following questions briefly and concisely.
Write your answer on the answer sheet provided.
1. Why are connections and relationships important in understanding
social networks?
2. What are the different types of network? Define each
3. Why is it important to study social networks?
4. How are neural networks similar to the brain?
5. How does neural network used to solve computational problems?
Write your answer on the answer sheet provided.
1. Why are connections and relationships important in understanding
social networks?
2. What are the different types of network? Define each
3. Why is it important to study social networks?
4. How are neural networks similar to the brain?
5. How does neural network used to solve computational problems?
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Answer:
1. In a professional setting, relationship-building helps create opportunities for individuals to collaborate, idea share, get feedback and reduce stress through social support. ... Social networks present great opportunities for professionals to publicly recognize successes of parties in the relationship.
2. The Network allows computers to connect and communicate with different computers via any medium. LAN, MAN and WAN are the three major types of the network designed to operate over the area they cover. There are some similarities and dissimilarities between them.
3. Social networks have many advantages in learning as they provide wide access to information and information resources, reduce barriers to group interaction and telecommunications
4. Artificial neural networks are more similar to the brain than they get credit for. ... Our brains, honed through millions of years of evolution, are very efficient processing machines, sorting out the ton of information we receive through our sensory inputs, associating known items with their respective categories.
5.A neural network is a network of artificial neurons programmed in software. It tries to simulate the human brain, so it has many layers of “neurons” just like the neurons in our brain. ... These networks can be incredibly complex and consist of millions of parameters to classify and recognize the input it receives.