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#NLP interview questions

  • Q. What is topic modeling? Discuss key algorithms, working, applications, and the pros and cons
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Explore Questions by Topics
  • Computer Vision (1)
  • Generative AI (2)
  • Machine Learning Basics (18)
  • Deep Learning (52)
    • DL Basics (16)
    • DL Architectures (17)
      • Feedforward Network / MLP (2)
      • Sequence models (6)
      • Transformers (9)
    • DL Training and Optimization (17)
  • Natural Language Processing (27)
    • NLP Data Preparation (18)
  • Supervised Learning (115)
    • Regression (41)
      • Linear Regression (26)
      • Generalized Linear Models (9)
      • Regularization (6)
    • Classification (70)
      • Logistic Regression (10)
      • Support Vector Machine (9)
      • Ensemble Learning (24)
      • Other Classification Models (9)
      • Classification Evaluations (9)
  • Unsupervised Learning (55)
    • Clustering (37)
      • Distance Measures (9)
      • K-Means Clustering (9)
      • Hierarchical Clustering (3)
      • Gaussian Mixture Models (5)
      • Clustering Evaluations (6)
    • Dimensionality Reduction (9)
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  • Data Preparation (35)
    • Feature Engineering (30)
    • Sampling Techniques (5)
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Other Questions in #NLP interview questions
  • What are the Pros/Cons of Naive Bayes? 
  • What is Bayes’ Rule?
  • What is Simple Random Sampling?
  • What are Transformers? Discuss the evolution, advantages and major breakthroughs in transformer models
  • What are the key hyperparameters for a GBM model?
  • What is IDF? What do we need IDF?
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