1. What is deep learning? What can deep learning do that traditional machine-learning methods cannot?
2. 2. List and briefly explain different learning paradigms/ methods in AI.
3. 3. What is representation learning, and how does it relate to machine learning and deep learning?
4. 4. List and briefly describe the most commonly used ANN activation functions.
5. 5. What is MLP, and how does it work? Explain the function of summation and activation weights in MLP-type ANN.
Complete the above questions in one MS word document:
4. 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 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.
Complete the above question in one MS word document:
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.
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1. What exactly is deep learning? What is it that deep learning can do that regular machine-learning approaches cannot?
2. List and briefly explain various AI learning paradigms/methods.
3. What exactly is representation learning, and how does it differ from machine learning and deep learning?
4. List and briefly describe the most popular ANN activation functions.
5. 5. What exactly is MLP and how does it work? Explain how summing and activation weights work in MLP-type ANN.
Fill out the following questions in a single MS Word document:
4. 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
