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1. Given an empty BST consist of n nodes and and an array consist of n numbers. The n nodes in a BST have been already arranged in some fashion(i.e. the BST is not empty), and none of the nodes in BST are having any data, that means we have to pick the n numbers from the given array and have to fill in the given BST. We have to make sure that the structure of the BST doesn't change. That means all the left subtree and right subtree at any given node should not change at all. 2. We have a function which returns a value among {1, 0, -1}. When the function returns -1 that means we have to terminate. we have to keep on calling this function and till we get -1. this means we will get series of 1's and 0's which we have to treat like bit pattern and has to check whether the given number is divisible by 3 or not. for e.g. the function call returns the below output. 101-1=> 101 => it's a 5 which is not divisible by 3.
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Computer Scientist

Interviewed at Adobe

4.1★
Sep 11, 2016

1. Given an empty BST consist of n nodes and and an array consist of n numbers. The n nodes in a BST have been already arranged in some fashion(i.e. the BST is not empty), and none of the nodes in BST are having any data, that means we have to pick the n numbers from the given array and have to fill in the given BST. We have to make sure that the structure of the BST doesn't change. That means all the left subtree and right subtree at any given node should not change at all. 2. We have a function which returns a value among {1, 0, -1}. When the function returns -1 that means we have to terminate. we have to keep on calling this function and till we get -1. this means we will get series of 1's and 0's which we have to treat like bit pattern and has to check whether the given number is divisible by 3 or not. for e.g. the function call returns the below output. 101-1=> 101 => it's a 5 which is not divisible by 3.

(1) Back-propagation; (2) overfit vs underfit; (3) implementation of one of the steps in Canny edge detection using graphs; (4) closed form formula to solution of linear regression; (5) write code for K nearest neighbor algorithm (6) vanishing gradient vs exploding gradients due to choice to relu() vs sigmoid()
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Computer Vision Engineer

Interviewed at Flipkart

3.8★
Oct 13, 2016

(1) Back-propagation; (2) overfit vs underfit; (3) implementation of one of the steps in Canny edge detection using graphs; (4) closed form formula to solution of linear regression; (5) write code for K nearest neighbor algorithm (6) vanishing gradient vs exploding gradients due to choice to relu() vs sigmoid()

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