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DL Architectures

  • Q. Making Transformers Work: Scale, Access, Deployment and Ethics
  • Q. What is a Multilayer Perceptron (MLP) or a Feedforward Neural Network (FNN)?
  • Q. What are Sequence Models? Discuss the key Sequence modeling algorithms and their real world applications
  • Q. Sequence Models Compared: RNNs, LSTMs, GRUs, and Transformers
  • Q. What are the advantages and disadvantages of a Recurrent Neural Network (RNN)?
  • Q. What are Transformers? Discuss the evolution, advantages and major breakthroughs in transformer models
  • Q. Explain the Transformer Architecture (with Examples and Videos)
  • Q. Understanding the architecture of Recurrent Neural Networks (RNN)
  • Q. What is Long-Short Term Memory (LSTM)?
  • Q. What are the primary advantages of transformer models?
  • Q. What are the limitations of transformer models?
  • Q. Explain Self-Attention, and Masked Self-Attention as used in Transformers
  • Q. Cross-Attention vs Self-Attention Explained
  • Q. What do you mean by Sequence data? Discuss the different types
  • Q. Multi-Head Attention: Why It Outperforms Single-Head Models
  • Q. Explain the need for Positional Encoding in Transformer models
  • Q. How are Regression and Classification performed using multilayer perceptrons (MLP)? 
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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 Architectures
  • What are options to calibrate probabilities produced from the output of a classifier that does not produce natural probabilities?
  • What are the subtypes of Cross Validation?
  • What is Specificity?
  • Explain the concept and working of the Random Forest model
  • How does a learning curve give insight into whether the model is under- or over-fitting?
  • What is the difference between Discriminative and Generative models?
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