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4. Suppose we have loaded a rectangular matrix X as the MNIST training dataset, 60000 samples, with 784 features (flattened from the pixels of 28 x 28 grayscale images). Suppose then that we normalized X so that each of its entries range from 0 to 1. Consider the following code snippets and answer the questions that follow. from numpy.linalg import svd u, s, vh - svd (X) #singlular value decomposition

4. Suppose we have loaded a rectangular matrix X as the MNIST training dataset, 60000 samples, with 784 features (flattened from the pixels of 28 x 28 grayscale images). Suppose then that we normalized X so that each of its entries range from 0 to 1. Consider the following code snippets and answer the questions that follow. from numpy.linalg import svd u, s, vh - svd (X) #singlular value decomposition

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Need formula for marirx X as described and run in the code given 

X1 - np.matmul (u[:,:784] +s, vh) np. allclose (X1,X)
Transcribed Image Text:X1 - np.matmul (u[:,:784] +s, vh) np. allclose (X1,X)
4. Suppose we have loaded a rectangular matrix X as the MNIST training dataset, 60000 samples, with 784 features (flattened from the pixels of 28 x 28 grayscale images). Suppose then that we normalized X so that each of its entries range from 0 to 1. Consider the following code snippets and answer the questions that follow. from numpy.linalg import svd u, s, vh - svd (X) #singlular value decomposition
Transcribed Image Text:4. Suppose we have loaded a rectangular matrix X as the MNIST training dataset, 60000 samples, with 784 features (flattened from the pixels of 28 x 28 grayscale images). Suppose then that we normalized X so that each of its entries range from 0 to 1. Consider the following code snippets and answer the questions that follow. from numpy.linalg import svd u, s, vh - svd (X) #singlular value decomposition
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