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248 management trainee 2018 interview questions shared by candidates

Implement Max Pooling Algorithm In Neural Networks used for Visual Recognition some layers perform a Max Pooling operation on an image represented as an NxM matrix of intensities (most often it is squared but we do not make this assumption in this task). Max Pooling consists of the following: given a window of size KxL, “slide” the window through the array without overlapping and return the maximum values for each window. It’s best explained with an example: Given a 4x4 matrix 1 1 2 4 5 6 7 8 3 2 1 0 1 2 3 4 and a square window of size 2x2, the subregions are: 1 1 | 2 4 5 6 | 7 8 -------- 3 2 | 1 0 1 2 | 3 4 The corresponding Max Pooled values for each subregions are: 6 8 3 4 A square window of size 1x1 will return the original matrix. For this task we will make the following assumptions: Matrices will only contain integers in the range (-1000, 1000) Matrices are always 2 dimensional (but not always square) and non-empty We will always use a square window (so we’ll provide only 1 size) The output should be the sum of Max Pooled values (e.g., 21 for the example above) If the window cannot fit into the matrix without overlapping, the output should be the string “NONE” Input: The rows of the matrix each on a separate line, followed by a blank line, and then a single number for the Max Pooling window size. Output: Print to standard output the sum of the Max Pooled values, or the string “NONE” if assumptions are not fulfilled. Test 1 Input 20 200 -13 134 120 32 -120 124 Expected Output: NONE Test 2 Test Input 20 200 -13 134 120 32 -120 12 Expected Output Output: 320 Test 3 Test Input: 1 1 2 4 5 6 7 8 3 2 1 0 1 2 3 4 Expected Output 21
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2018 Cognitive Software Developer Internship

Interviewed at IBM

3.9★
Feb 23, 2018

Implement Max Pooling Algorithm In Neural Networks used for Visual Recognition some layers perform a Max Pooling operation on an image represented as an NxM matrix of intensities (most often it is squared but we do not make this assumption in this task). Max Pooling consists of the following: given a window of size KxL, “slide” the window through the array without overlapping and return the maximum values for each window. It’s best explained with an example: Given a 4x4 matrix 1 1 2 4 5 6 7 8 3 2 1 0 1 2 3 4 and a square window of size 2x2, the subregions are: 1 1 | 2 4 5 6 | 7 8 -------- 3 2 | 1 0 1 2 | 3 4 The corresponding Max Pooled values for each subregions are: 6 8 3 4 A square window of size 1x1 will return the original matrix. For this task we will make the following assumptions: Matrices will only contain integers in the range (-1000, 1000) Matrices are always 2 dimensional (but not always square) and non-empty We will always use a square window (so we’ll provide only 1 size) The output should be the sum of Max Pooled values (e.g., 21 for the example above) If the window cannot fit into the matrix without overlapping, the output should be the string “NONE” Input: The rows of the matrix each on a separate line, followed by a blank line, and then a single number for the Max Pooling window size. Output: Print to standard output the sum of the Max Pooled values, or the string “NONE” if assumptions are not fulfilled. Test 1 Input 20 200 -13 134 120 32 -120 124 Expected Output: NONE Test 2 Test Input 20 200 -13 134 120 32 -120 12 Expected Output Output: 320 Test 3 Test Input: 1 1 2 4 5 6 7 8 3 2 1 0 1 2 3 4 Expected Output 21

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