The result of the subtraction of 4365 and 3412 using octal numbers is of:
753.
<h3>What are octal numbers?</h3>
For octal numbers, each number is represented by three bits, as follows:
Then the binary representation of number 4365 is given as follows:
100011110101
The binary representation of number 3412 is given as follows:
011100001010
Then the following binary subtraction is made:
100011110101 - 011100001010
The result of this subtraction is of:
100011110101 - 011100001010 = 111101011
Then the octal result is given as follows:
111 101 011 = 753.
More can be learned about binary numbers at brainly.com/question/8649831
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Answer:
The answer is D. In order to make an advertisement, the photo or photos have to be clear and easy to see. They also have to stay clear when they are enlarged
Explanation:
Concurrent multiple program execution, opposite to pipeline execution where it needs to in a sequential or in order of execution. You can also say parallel process programming.
Bayes’ Theorem provides a way that we can calculate the probability of a piece of data belonging to a given class, given our prior knowledge.
P(class|data) = (P(data|class) * P(class)) / P(data)
Where P(class|data) is the probability of class given the provided data.
Explanation:
- Naive Bayes is a classification algorithm for binary and multiclass classification problems.
- It is called Naive Bayes or idiot Bayes because the calculations of the probabilities for each class are simplified to make their calculations tractable.
This Naive Bayes tutorial is broken down into 5 parts:
Step 1: Separate By Class : Calculate the probability of data by the class they belong to, the so-called base rate. Separate our training data by class.
Step 2: Summarize Dataset : The two statistics we require from a given dataset are the mean and the standard deviation
The mean is the average value and can be calculated using :
mean = sum(x)/n * count(x)
Step 3: Summarize Data By Class : Statistics from our training dataset organized by class.
Step 4: Gaussian Probability Density Function : Probability or likelihood of observing a given real-value. One way we can do this is to assume that the values are drawn from a distribution, such as a bell curve or Gaussian distribution.
Step 5: Class Probabilities : The statistics calculated from our training data to calculate probabilities for new data. Probabilities are calculated separately for each class. This means that we first calculate the probability that a new piece of data belongs to the first class, then calculate the second class, on for all the classes.
