Deep Learning Engineer

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Deep Learning Engineer Interview Questions and Answers

Updated 10 Jun 2024

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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.

Deep Learning Engineer Interview Questions and Answers for Freshers

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Q3. What are different types of regression analysis ?

Ans.

Different types of regression analysis include linear regression, logistic regression, polynomial regression, ridge regression, and lasso regression.

  • Linear regression: Predicts a continuous outcome based on one or more input features.

  • Logistic regression: Predicts the probability of a binary outcome.

  • Polynomial regression: Fits a curve to the data by including polynomial terms.

  • Ridge regression: Adds a penalty term to the linear regression to prevent overfitting.

  • Lasso regression...read more

Q4. Different NLP techniques around extraction of text.

Ans.

Various NLP techniques for text extraction include Named Entity Recognition, Part-of-Speech tagging, and Dependency Parsing.

  • Named Entity Recognition (NER) identifies entities such as names, dates, and locations in text.

  • Part-of-Speech tagging assigns grammatical categories to words in a sentence.

  • Dependency Parsing analyzes the grammatical structure of a sentence to identify relationships between words.

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Q5. Explain the architecture of Transformer based models.

Ans.

Transformer based models use self-attention mechanism to capture long-range dependencies in data.

  • Transformer models consist of encoder and decoder layers.

  • Self-attention mechanism allows each word to attend to all other words in the input sequence.

  • Positional encoding is added to input embeddings to provide information about the position of words.

  • Transformer models have achieved state-of-the-art results in various NLP tasks such as machine translation, text generation, and sent...read more

Q6. What is cnn network,dropout,weight initialization techniques, why relu ?

Ans.

CNN is a type of neural network commonly used for image recognition. Dropout is a regularization technique to prevent overfitting. Weight initialization techniques are methods to set initial weights in a neural network. ReLU is a popular activation function due to its ability to address the vanishing gradient problem.

  • CNN (Convolutional Neural Network) is commonly used for image recognition tasks due to its ability to capture spatial hierarchies in data.

  • Dropout is a regulariza...read more

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Q7. How to create embedding from base and can we create text embedding from pretrained model

Ans.

Yes, embeddings can be created from base using techniques like Word2Vec, GloVe, or FastText. Text embeddings can also be created from pretrained models like BERT or Word2Vec.

  • Use techniques like Word2Vec, GloVe, or FastText to create embeddings from base data

  • Pretrained models like BERT or Word2Vec can be used to create text embeddings

  • Fine-tuning pretrained models can also be done to create custom text embeddings

Q8. What is confusion matrix?

Ans.

Confusion matrix is a table used to evaluate the performance of a classification model.

  • It is a matrix with rows representing the actual class and columns representing the predicted class.

  • It helps in visualizing the performance of a classification model by showing the counts of true positive, true negative, false positive, and false negative predictions.

  • It is commonly used in machine learning to assess the quality of the classification model.

  • Example: In a binary classification...read more

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Q9. 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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