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DL Training and Optimization

  • Q. What is Parameter Efficient Fine-Tuning (PEFT)?
  • Q. What is an activation function? What are the different types of activation functions? Discuss their pros and cons
  • Q. What is Rectified Linear Unit (ReLU) activation function? Discuss its advantages and disadvantages
  • Q. What is the “dead ReLU” problem and, why is it an issue in Neural Network training?
  • Q. What are some strategies to address Overfitting in Neural Networks?
  • Q. What is Dropout?
  • Q. Describe briefly the training process of a Neural Network model
  • Q. What do you mean by saturation in neural network training? Discuss the problems associated with saturation
  • Q. What is the vanishing and exploding gradient problem, and how are they typically addressed?
  • Q. Why is Zero-centered output preferred for an activation function?
  • Q. What are the key hyper-parameters of a neural network model?
  • Q. What is an activation function, and what are some of the most common choices for activation functions?
  • Q. What are some options for making Backpropagation more efficient?
  • Q. Discuss Softmax activation function
  • Q. What is Sigmoid (logistic) activation function?
  • Q. Discuss TanH activation function
  • Q. What are some guidelines for choosing activation functions?
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    • DL Basics (16)
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      • Sequence models (6)
      • Transformers (9)
    • DL Training and Optimization (17)
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    • NLP Data Preparation (18)
  • Supervised Learning (115)
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      • Regularization (6)
    • Classification (70)
      • Logistic Regression (10)
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      • Ensemble Learning (24)
      • Other Classification Models (9)
      • Classification Evaluations (9)
  • Unsupervised Learning (55)
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      • Gaussian Mixture Models (5)
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Other Questions in DL Training and Optimization
  • 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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