Consider Greg Linden's illustration of search engines learning by observing which search results users click. What are the advantages of having this information for the search engine company (e.g., Google or Microsoft in Linder's examples)?
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Consider Greg Linden's illustration of search engines learning by observing which search results users click. What are the advantages of having this information for the search engine company (e.g., Google or Microsoft in Linder's examples)?
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- If you could design email, what would it look like? How can an electronic mail message physically go from one place to another? Make a tally of everything you have discovered. Specifically, how are they different, and what motivated the decision to make those differences in the first place? We may safely assume that there is a wide range in the complexity of the models (or abstraction).watch the following videos from the YouTube channel StatQuest:● Machine Learning Fundamentals: Sensitivity and Specificity● ROC and AUC, Clearly Explained!After reading and watching those videos, you should write a short essay that answers the followingquestions in your own words:● What is precision, recall, sensitivity, and specificity? How would you explain it to someone new tothe field of machine learning?● What is the precision/recall trade-off? Provide an unique example to illustrate the concept.● Explain the ROC curve and how it is used.When you hear the word "email," what are some of the first things that spring to mind? What happens to an email once it has been sent? Make a note of all the things you have learned that you didn't know previously. I'm curious if anybody has any ideas on what may be causing these discrepancies. Does your organization have a range of model complexity?
- What comes to mind when you think of email? What occurs after an email has been sent? Make a note of everything you have learned up to this point. Does anybody know what these disparities' root causes are, and if so, what they are? Do your models support various degrees of complexity?What image of e-mail do you have in your mind? How does a message travel to its intended recipient after it is sent? Make a mental note of all you've learned. What are the differences and why do they exist? Consider the level of complexity (or abstraction) that various types of models contain.What pictures come to mind when you hear the term "email"? When an email is sent, what happens to it? Gather all the information you've gleaned to date and write it down. I'm wondering if anybody can provide any light on why these inconsistencies exist. Is there a wide range of complexity in the models you build?
- What picture do you have in your head when you think about e-mail? When a message is sent, how does it get to the person who is supposed to receive it? Make a note of all of the information you've gathered thus far. What are the differences, and why are they important to understand? Consider the degree of detail (or the level of abstraction) that various types of models possess in comparison to one another.You're almost certainly familiar with two-dimensional spreadsheets, such as those created in Microsoft Excel or similar programs. Do you consider a spreadsheet like this to be a "computational modelTM" in your mind? Could you please elaborate on your response?What image do you have of an email in your mind? An email's journey begins and ends at the same area. Keep track of your findings. Why are there disparities in the first place? Have you noticed the variations in model detail (or abstraction)?
- What types of pictures spring to mind for you when you think about email? What exactly happens behind the scenes when an email is sent? Make a list with every piece of information you have discovered so far. Does anybody know what the fundamental causes of these disparities are? Does your model function well at different levels of complexity?As compared to deep learning, where does machine learning stand?How do you describe Asynchronous E-Learning?