Question: 1)Explain the MNIST dataset used. What is its 2D structure? Its flattened structure?(Lecture 1) 2)Explain the score function or the classifier. What are its parameters?(Lecture

1)Explain the MNIST dataset used. What is its 2D structure? Its flattened structure?(Lecture 1)

2)Explain the score function or the classifier. What are its parameters?(Lecture 1)

3)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 programming language is used?

4)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.(Lecture 1)

5)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.

6)The main algorithm that makes Neural Networks capable of understanding data is the gradient descent algorithm. Explain in simple terms this algorithm. How is the gradient used in the algorithm?(Lecture 5)

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