Innomatics Research Labs Data Science Intern Interview Questions and Answers

Q1. ML and deep learning questions
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Q1. Projects discussion
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Q2. Chatgpt architecture
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Basic DSA questions will be asked Leetcode Easy to medium

Q1. BERT vs LSTM and their speed
Ans. 
BERT is faster than LSTM due to its transformer architecture and parallel processing capabilities.
• BERT utilizes transformer architecture which allows for parallel processing of words in a sentence, making it faster than LSTM which processes words sequentially.

• BERT has been shown to outperform LSTM in various natural language processing tasks due to its ability to capture long-range dependencies more effectively.

• For exa...read more
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Q2. What is multinomial Naive Bayes theorem
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Multinomial Naive Bayes is a classification algorithm based on Bayes' theorem with the assumption of independence between features.
• It is commonly used in text classification tasks, such as spam detection or sentiment analysis.

• It is suitable for features that represent counts or frequencies, like word counts in text data.

• It calculates the probability of each class given the input features and selects the class with the
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Q1. What are Large Language Models?
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Large Language Models are advanced AI models that can generate human-like text based on input data.
• Large Language Models use deep learning techniques to understand and generate text.

• Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).

• They are trained on vast amounts of text data to improve their language generation capabilities.
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Q2. Do you know about RAGs?
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RAGs stands for Red, Amber, Green. It is a project management tool used to visually indicate the status of tasks or projects.
• RAGs is commonly used in project management to quickly communicate the status of tasks or projects.

• Red typically indicates tasks or projects that are behind schedule or at risk.

• Amber signifies tasks or projects that are on track but may require attention.

• Green represents tasks or projects that ar...read more
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Q3. Which is the best clustering algorithm?
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There is no one-size-fits-all answer as the best clustering algorithm depends on the specific dataset and goals.
• The best clustering algorithm depends on the dataset characteristics such as size, dimensionality, and noise level.

• K-means is popular for its simplicity and efficiency, but may not perform well on non-linear data.

• DBSCAN is good for clusters of varying shapes and sizes, but may struggle with high-dimensional d...read more
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