Learning and development Interview Questions
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Learning And Development interview questions shared by candidates
Suppose you have a matrix of numbers. How can you easily compute the sum of any rectangle (i.e. a range [row_start, row_end, col_start, col_end]) of those numbers? How would you code this?
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Compute the sum of the rectangles, for all i,j, bounded by (i,j), (i,m), (n,j), (n,m), where (n,m) is the size of the matrix M. Call that sum s(i,j). You can calculate s(i,j) by dynamic programming: s(i,j) = M(i,j) + s(i+1,j) + s(i,j+1) - s(i+1,j+1). And the sum of any rectangle can be computed from s(i,j). Less
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Awesome!!
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The answer is already popular in computer vision fields!! It is called integral imaging. See this page http://en.wikipedia.org/wiki/Haar-like_features Less
Have you ever had your code formally verified?
6 Answers↳
What were the online coding questions like? Could you elaborate?
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Object detection. Is that what yours was?
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it is same as mine. Could you give me more details about the online coding? what algorithm did they test on object detection part? Less
There are 10 stacks of 10 coins each. Each coin weighs 10 gms. However, one stack of coins is defective and each coin in that stack weights only 9 gms. What is the minimum number of weights you need to take to find which stack is defective? How?
5 Answers↳
You can also apply a binary search. which will give you the answer minimum 3 and max 4 Less
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Can be done in 2 weights
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Can be done in 2 weights.
What are some of the projects that you have done?
4 Answers↳
Do you mind to share what are the hard leetcode questions they asked during the interview? Less
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I dont think it's fair to share which question they asked. But the exact same question is on leetcode and the difficulty level is hard. Less
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What topic you are being ask from in leetcode? also did they ask you system design and CS fundamentals. Less
Give an image, when we take 2 sub images from it, calculate the ratio similar to AnB/AuB.
4 Answers↳
Coded in python but wasn't able to finish it
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Can you elaborate on the question
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Given a matrix and coordinates of 2 rectangles calculate the weighted IoU in linear/constant time. Less
Why do you want to join Bajaj even though you are in a very good profile right now?
3 Answers↳
Yes
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Yes
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Yes
Probability, conventional machine learning, deep learning, coding/algorithm, behavioral
3 Answers↳
I was good with most of the questions
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Can you share how coding round was? what type of questions were asked?
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Can you share what they asked in the coding round?
how to sort in O(Logn) time
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I don't think you can sort in O(logn) because you will need to go through the whole data at least once, making it O(n). Indeed, you can do it in O(logn) if the data is guarantee with some specific constrain or relationship. I think the best you can sort a completely random data is O(nlogn). Less
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I didn't come up with the answer. it is not difficult, just not prepared
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what is the question
Why does one use MSE as a measure of quality. What is the scientific/mathematical reason for the same?
3 Answers↳
Mean-Square error is an error metric for measuring image or video quality it is popular video and image quality metric because the analysis and mathematics is easier with this L2-Norm metric. Most video and image quality experts will agree that MSE is not a very good measure of perceptual video and image quality. Less
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The mathematical reasoning behind the MSE is as follows: For any real applications, noise in the readings or the labels is inevitable. We generally assume this noise follows Gaussian distribution and this holds perfectly well for most of the real applications. Considering 'e' follows gaussian distribution in y=f(x) + e and calculating the MLE, we get MSE which is also L2 distance. Note: Assuming some other noise distribution may lead to other MLE estimate which will not be MSE. Less
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MSE is used for understanding the weight of the errors in any model. This helps us understand model accuracy in a way that is helpful when choosing different types of models. Check out more answers on InterviewQuery.com Less