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  • Computer Vision (1)
  • Generative AI (2)
  • Machine Learning Basics (18)
  • Deep Learning (52)
    • DL Basics (16)
    • DL Architectures (17)
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      • Other Classification Models (9)
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      • Clustering Evaluations (6)
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  • Statistics (34)
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    • Feature Engineering (30)
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DL Basics

  • Q. What is a Vector Database and How is it used for RAG?
  • Q. What is Knowledge Distillation?
  • Q. Explain 𝐑𝐎𝐔𝐆𝐄 𝐚𝐧𝐝 𝐢𝐭s 𝐢𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐭 𝐢𝐧 𝐍𝐋𝐏
  • Q. What is Instruction Fine-Tuning
  • Q. What is Convolution?
  • Q. Explain Perplexity
  • Q. What is Precision@K?
  • Q. Explain the basic architecture of a Neural Network, model training and key hyper-parameters
  • Q. What is Deep Learning? Discuss the key characteristics, working and applications of Deep Learning
  • Q. What are the advantages and disadvantages of Deep Learning?
  • Q. How does Deep Learning methods compare with traditional Machine Learning methods?
  • Q. What is a Perceptron? What is the role of bias in a perceptron (or neuron)?
  • Q. What do you mean by pretraining, finetuning and transfer learning?
  • Q. What is Backpropagation? 
  • Q. What is the difference between a Batch and an Epoch?
  • Q. What is the difference between Deep and Shallow networks?
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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)
  • Statistics (34)
  • Data Preparation (35)
    • Feature Engineering (30)
    • Sampling Techniques (5)
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Other Questions in DL Basics
  • How does hinge loss differ from logistic loss?
  • Describe briefly the training process of a Neural Network model
  • How does pruning a tree work?
  • Explain Perplexity
  • What are some pros and cons of Discriminant Analysis?
  • What is the difference between a Probability Mass Function (PMF), Probability Density Function (PDF), and Cumulative Distribution Function (CDF)?
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