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Deep Learning

Q. What is Parameter Efficient Fine-Tuning (PEFT)?
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. What is Supervised Fine-Tuning?
Q. Making Transformers Work: Scale, Access, Deployment and Ethics
Q. Understanding the architecture of Recurrent Neural Networks (RNN)
Q. What do you mean by pretraining, finetuning and transfer learning?
Q. What is Deep Learning? Discuss the key characteristics, working and applications of Deep Learning
Q. What are the primary advantages of transformer models?
Q. Explain Self-Attention, and Masked Self-Attention as used in Transformers
Q. Explain the need for Positional Encoding in Transformer models
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. Top 20 Deep Learning Interview Questions with detailed Answers (All free)
Q. What is the “dead ReLU” problem and, why is it an issue in Neural Network training?
Q. Multi-Head Attention: Why It Outperforms Single-Head Models
Q. Cross-Attention vs Self-Attention Explained
Q. Explain the Transformer Architecture (with Examples and Videos)
Q. Why is Zero-centered output preferred for an activation function?
Q. What is the vanishing and exploding gradient problem, and how are they typically addressed?
Q. What do you mean by saturation in neural network training? Discuss the problems associated with saturation
Q. What is an activation function? What are the different types of activation functions? Discuss their pros and cons
Q. What are the key hyper-parameters of a neural network model?
Q. Describe briefly the training process of a Neural Network model
Q. What do you mean by Sequence data? Discuss the different types
Q. What are the limitations of transformer models?
Q. What are Transformers? Discuss the evolution, advantages and major breakthroughs in transformer models
Q. What is Long-Short Term Memory (LSTM)?
Q. What is the difference between a Batch and an Epoch?
Q. What is Dropout?
Q. What are some strategies to address Overfitting in Neural Networks?
Q. What are some options for making Backpropagation more efficient?
Q. What is Backpropagation?
Q. How are Regression and Classification performed using multilayer perceptrons (MLP)?
Q. What are some guidelines for choosing activation functions?
Q. Discuss Softmax activation function
Q. What is Rectified Linear Unit (ReLU) activation function? Discuss its advantages and disadvantages
Q. Discuss TanH activation function
Q. What is Sigmoid (logistic) activation function?
Q. What is an activation function, and what are some of the most common choices for activation functions?
Q. What is the difference between Deep and Shallow networks?
Q. Explain the basic architecture of a Neural Network, model training and key hyper-parameters
Q. What is a Multilayer Perceptron (MLP) or a Feedforward Neural Network (FNN)?
Q. What is a Perceptron? What is the role of bias in a perceptron (or neuron)?
Q. What are the advantages and disadvantages of Deep Learning?
Q. How does Deep Learning methods compare with traditional Machine Learning methods?

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Explore Questions by Topics

Computer Vision (1)
Data Preparation (35)
- Feature Engineering (30)
- Sampling Techniques (5)
Deep Learning (52)
- DL Architectures (17)
  - Feedforward Network / MLP (2)
  - Sequence models (6)
  - Transformers (9)
- DL Basics (16)
- DL Training and Optimization (17)
Generative AI (2)
Machine Learning Basics (18)
Natural Language Processing (27)
- NLP Data Preparation (18)
Statistics (34)
Supervised Learning (115)
- Classification (70)
  - Classification Evaluations (9)
  - Ensemble Learning (24)
  - Logistic Regression (10)
  - Other Classification Models (9)
  - Support Vector Machine (9)
- Regression (41)
  - Generalized Linear Models (9)
  - Linear Regression (26)
  - Regularization (6)
Unsupervised Learning (55)
- Clustering (37)
  - Clustering Evaluations (6)
  - Distance Measures (9)
  - Gaussian Mixture Models (5)
  - Hierarchical Clustering (3)
  - K-Means Clustering (9)
- Dimensionality Reduction (9)

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Other Questions in Deep Learning

How does SVM adjust for classes that cannot be linearly separated?
What is Bi-Clustering? What are possible use cases of it?
How to perform Standardization in case of outliers?
What is a Multilayer Perceptron (MLP) or a Feedforward Neural Network (FNN)?
Distinguish between a Weak learner and a Strong Learner
What are the pros and cons of parametric vs. non-parametric models?

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