Deep Learning Engineer

Deep Learning Engineer Interview Questions and Answers for Freshers

Updated 11 Mar 2022

Q1. Graph Coloring Problem

You are given a graph with 'N' vertices numbered from '1' to 'N' and 'M' edges. Your task is to color this graph using two colors, such as blue and red, in a way that no two adjacent vert...read more

Ans.

The task is to color a graph with two colors in a way that no two adjacent vertices share the same color.

  • Check if the graph can be colored using two colors without any adjacent vertices sharing the same color.

  • Use graph coloring algorithms like Greedy Coloring or Backtracking to solve the problem.

  • Consider connected components in the graph while coloring.

  • If a vertex has an odd cycle, it is not possible to color the graph with two colors.

Q2. Wildcard Pattern Matching Problem Statement

Implement a wildcard pattern matching algorithm to determine if a given wildcard pattern matches a text string completely.

The wildcard pattern may include the charac...read more

Ans.

Implement a wildcard pattern matching algorithm to determine if a given wildcard pattern matches a text string completely.

  • Create a recursive function to match the pattern with the text character by character.

  • Handle cases for '?' and '*' characters in the pattern.

  • Use dynamic programming to optimize the solution.

  • Check for edge cases like empty pattern or text.

  • Iterate through the pattern and text to determine if they match completely.

Q3. What is the loss function of YOLO?
Ans.

The loss function of YOLO is a combination of localization loss, confidence loss, and classification loss.

  • YOLO uses a sum-squared error loss function to calculate the localization loss, which penalizes the difference between the predicted bounding box and the ground truth bounding box.

  • The confidence loss penalizes the model for incorrect objectness predictions and no object predictions in grid cells.

  • The classification loss is calculated using a softmax function to predict the...read more

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