Explain the two basic functions from algebra that forms the basis for a score function
Write a 2-3 pages(at least 800 words) paper on a statistic topic of your choosing. Your paper needs to include at least one topic that is an application of a conceptthat we covered this semester, for example, applications of linear regression, normal distribution, probability, hypothesis testing, etc… See below for some sample topics. Your paper needs to have an analysis or explanation of at least one math/statistical equation. Please cite all your references. Specifically, you must use in-text citations for any information you obtained from another source, including math formulas and discussions of those formulas.
Explain the MNIST dataset used. What is its 2D structure? Its flattened structure?
Explain the score function or the classifier. What are its parameters?
Explain the two basic functions from algebra that forms the basis for a score function. Explain the coding demonstration from the video(Lecture 1). What language is used?
Explain the process of training the computer to learn patterns found in images. Why is this process a data-driven approach? Discuss the terms: training set, the testing set, true labels or the ground truth.
What is a derivative? Explains and give a simple geometric interpretation. What is a gradient? Give a geometric interpretation of the gradient. Use the analogy from Lecture 4.
The main algorithm that makes Neural Networks capable of understanding data is the gradient descent algorithm. Explain this algorithm. How is the gradient used in the algorithm?
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