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Answer:
Training data is used to fine-tune the algorithm’s parameters and evaluate how good the model is
Explanation:
The statement about datasets used in Machine Learning that is NOT true is "Training data is used to fine-tune algorithm’s parameters and evaluate how good the model is."
This is based on the fact that a Training dataset is a process in which a dataset model is trained for corresponding it essentially to fit the parameters.
Also, Testing the dataset is a process of examining the performance of the dataset. This refers to hidden data for which predictions are determined.
And Validation of dataset is a process in which results are verified to perfect the algorithm's details or parameters
Answer:
All of these choices
Explanation:
Clarity → the quality of being coherent and intelligible.
Sincerity → the quality of being free from pretense, deceit, or hypocrisy
Open mind → Open-mindedness is receptiveness to new ideas.
Answer:
A) Number of bits for byte = 6 bits
B) number of bits for index = 17 bits
C) number of bits for tag = 15 bits
Explanation:
Given data :
cache size = 64 kB
block size = 32 -byte
block address = 32 -bit
number of blocks in cache memory
cache size / block size = 64 kb / 32 b = 2^11 hence the number of blocks in cache memory = 11 bits = block offset
A) Number of bits for byte
= 6 bits
B) number of bits for index
block offset + byte number
= 11 + 6 = 17 bits
c ) number of bits for tag
= 32 - number of bits for index
= 32 - 17 = 15 bits
A speaker sometimes is not able to capture the intended details since it is affected by homophones.
