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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 importance 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? Key Features, Working & Applications
  • 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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      • Sequence models (6)
      • Transformers (11)
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      • Linear Regression (26)
      • Generalized Linear Models (9)
      • Regularization (6)
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Other Questions in DL Basics
  • What Are the Core Components of an Agentic System?
  • How Learning Rate Scheduling Works (with PyTorch Examples)
  • What is Spectral co-clustering?
  • What is Chebyshev’s Theorem and its implications?
  • Explain ResNet Architecture and Its Variants
  • What is Mean Imputation?
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