Deep Learning Engineer

Deep Learning Engineer Interview Questions and Answers

Updated 10 Jun 2024

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Q1. Wildcard Pattern Matching

Given a text and a wildcard pattern of size N and M respectively, implement a wildcard pattern matching algorithm that finds if the wildcard pattern is matched with the text. The matchi...read more

Q2. Colour The Graph

You are given a graph with 'N' vertices numbered from '1' to 'N' and 'M' edges. You have to colour this graph in two different colours, say blue and red such that no two vertices connected by an...read more

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. Deep Learning Question

Why is attention needed? how do we evaluate attention for Seq2Seq models? Is the attention evaluated at once?

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Q5. Deep Learning Question

What is the Loss function of YOLO?

Q6. 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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Q7. 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

Q8. 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

Deep Learning Engineer Jobs

Ignitarium Technology Solutions - Deep Learning Engineer - OpenCV/C++/Tensorflow (3-8 yrs) • 3-8 years

Ignitarium Technology Solutions Private Limited

•

3.7

₹ 10 L/yr - ₹ 24 L/yr

Deep Learning Engineer - I • 4-9 years

Detect Technologies

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3.9

Chennai

Image Processing & Deep Learning Engineer - Generative AI (2-10 yrs) • 2-10 years

FLIXSTOCK INDIA PRIVATE LIMITED

•

3.1

₹ 20 L/yr - ₹ 25 L/yr

Delhi ncr

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Q9. 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

Q10. 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