Showing posts with label #Advanced. Show all posts
Showing posts with label #Advanced. Show all posts

13 September 2025

#GenAI

#GenAI
Level Topic Subtopics
Basic Generative AI What is Generative AI, History of Generative AI, Applications, Difference from Discriminative Models, Overview of Generative AI Models
AI & ML Fundamentals Basics of Machine Learning, Neural Networks, Supervised vs Unsupervised Learning, Reinforcement Learning, Probability & Statistics for Generative Models
Data Preparation Data Collection, Data Cleaning, Feature Engineering, Dataset Splits, Evaluation Metrics Basics
Tools & Frameworks Python, TensorFlow, PyTorch, Hugging Face Transformers, OpenAI APIs, Google Colab
Ethics & Safety Bias in Generative Models, Deepfakes, Misinformation, Responsible AI, Copyright Considerations
Intermediate Generative Models Variational Autoencoders (VAE), Generative Adversarial Networks (GAN), Conditional GANs, Diffusion Models, Flow-based Models
Text Generation Language Models, GPT Architecture, Tokenization, Text Preprocessing, Prompt Engineering
Image Generation Convolutional Networks, Image-to-Image Translation, Style Transfer, Image Augmentation, Pretrained Models
Model Training Loss Functions, Optimization, Gradient Descent, Regularization, Training Stability
Evaluation & Metrics Perplexity, FID Score, Inception Score, BLEU Score, Human Evaluation
Advanced Advanced Generative Architectures Transformers, Attention Mechanism, Diffusion Models, Large Language Models (LLMs), Multi-modal Models
Fine-Tuning & Adaptation Transfer Learning, Domain Adaptation, Parameter Efficient Fine-Tuning (PEFT), LoRA, Prompt Tuning
Multi-Modal Generative AI Text-to-Image, Text-to-Audio, Text-to-Video, Cross-Modal Retrieval, Embedding Spaces
Model Deployment & Scaling Serving Models, APIs, Latency Optimization, Distributed Training, Cloud Deployment
Security & Robustness Adversarial Attacks, Model Poisoning, Hallucinations, Bias Mitigation, Model Auditing
Expert State-of-the-Art Generative AI GPT-4/5, DALL-E, Stable Diffusion, MidJourney, Open-Source LLMs, Advanced Diffusion Techniques
Research & Innovation Self-Supervised Learning, Few-Shot & Zero-Shot Learning, Reinforcement Learning with Human Feedback (RLHF), AI Alignment, Generative Model Evaluation Research
Explainability & Interpretability SHAP, LIME, Counterfactual Explanations, Understanding Latent Spaces, Debugging Generative Models
Ethics, Governance & Policy Regulation of Generative AI, Deepfake Detection, Intellectual Property, Privacy-Preserving AI, Responsible Deployment Strategies
Performance & Optimization Mixed Precision Training, Memory Optimization, Inference Acceleration, Efficient Architectures, Energy-Efficient AI

1. Generative AI Basics

  1. What is Generative AI and how does it differ from discriminative AI?
  2. Explain common applications of Generative AI.
  3. How did Generative AI evolve over the years?
  4. Difference between supervised, unsupervised, and generative learning.
  5. Explain the concept of latent space in Generative AI.
  6. What are some real-world use cases of Generative AI?
  7. Explain the difference between deterministic and probabilistic models.
  8. What is the role of probability and statistics in Generative AI?
  9. Explain tokenization in NLP generative models.
  10. Difference between text generation, image generation, and multi-modal generation.
  11. What are ethical concerns in Generative AI?
  12. How does copyright affect AI-generated content?
  13. Explain model hallucinations in Generative AI.
  14. How do you evaluate a generative model qualitatively?
  15. Difference between narrow AI and generative AI capabilities.
  16. What is overfitting in Generative AI models?
  17. Explain underfitting in generative models.
  18. How do generative models learn patterns from data?
  19. Difference between pretraining and fine-tuning in Generative AI.
  20. What is prompt engineering in text generation?
  21. How do generative AI tools handle user data?
  22. Explain the role of embeddings in generative models.
  23. What is the difference between shallow and deep generative models?
  24. How do you preprocess data for Generative AI models?
  25. Explain evaluation metrics like BLEU and FID.

2. Intermediate Generative Models

  1. Explain Variational Autoencoders (VAE).
  2. What is a Generative Adversarial Network (GAN)?
  3. Difference between GAN and VAE.
  4. Explain Conditional GANs and their use cases.
  5. How do Diffusion Models work?
  6. What is Flow-based generative modeling?
  7. How do you implement tokenization for GPT-style models?
  8. Explain embedding representations in generative text models.
  9. Difference between RNN-based and Transformer-based generative models.
  10. How do you perform sequence generation?
  11. Explain beam search in text generation.
  12. What is temperature in text generation models?
  13. Explain the role of attention mechanism in generative models.
  14. How do you fine-tune a pre-trained generative model?
  15. Difference between supervised fine-tuning and reinforcement fine-tuning.
  16. What are prompt-based models?
  17. Explain training instability in GANs.
  18. How do you prevent mode collapse in GANs?
  19. How do you handle overfitting in VAEs?
  20. Difference between text-to-text and text-to-image generation.
  21. How do you evaluate generative models quantitatively?
  22. Explain Inception Score (IS) and Fréchet Inception Distance (FID).
  23. How do you handle multi-modal generative tasks?
  24. Explain the importance of data augmentation in generative training.
  25. What are the limitations of intermediate generative models?

3. Advanced Generative AI

  1. Explain transformer architecture for generative AI.
  2. How does self-attention work in transformers?
  3. What is GPT architecture and its key components?
  4. Difference between GPT and BERT.
  5. Explain large language models (LLMs).
  6. How do diffusion models generate high-quality images?
  7. Explain latent diffusion models.
  8. What is fine-tuning with LoRA (Low-Rank Adaptation)?
  9. How do you perform parameter-efficient fine-tuning (PEFT)?
  10. Difference between few-shot, zero-shot, and one-shot learning.
  11. Explain reinforcement learning with human feedback (RLHF).
  12. How do generative models handle multi-turn conversations?
  13. Explain text-to-image models like DALL-E or Stable Diffusion.
  14. What are attention-based cross-modal models?
  15. Explain embedding spaces for multi-modal AI.
  16. How do you optimize generative AI for inference speed?
  17. Explain memory-efficient training techniques.
  18. What are common failure modes in advanced generative models?
  19. How do you handle hallucinations in generative outputs?
  20. Explain evaluation metrics for advanced generative AI.
  21. How do you monitor and log generative model performance?
  22. Explain model distillation for large generative models.
  23. How do you perform continuous learning in generative AI?
  24. Explain token mixing and positional encoding.
  25. How do you deploy generative models in production?

4. Expert-Level Generative AI

  1. Explain GAN variants (StyleGAN, CycleGAN, BigGAN).
  2. How do advanced diffusion models work?
  3. Explain text-to-video generative models.
  4. How do generative AI models scale to billions of parameters?
  5. Explain multimodal foundation models.
  6. How do you implement RLHF at scale?
  7. Explain alignment challenges in generative AI.
  8. What is AI interpretability for generative models?
  9. How do you use SHAP or LIME for generative models?
  10. Explain model auditing and debugging in production.
  11. How do you detect and mitigate bias in generative AI?
  12. Explain privacy-preserving generative AI.
  13. How do you handle copyright and licensing issues for generated content?
  14. Explain federated learning for generative AI.
  15. How do you evaluate generative AI in open-ended tasks?
  16. Explain multimodal embeddings and cross-modal retrieval.
  17. How do you combine diffusion and transformer architectures?
  18. Explain advanced tokenization strategies for large models.
  19. How do you optimize large-scale training costs?
  20. Explain energy-efficient architectures for generative AI.
  21. How do you implement AI alignment techniques?
  22. How do you monitor hallucinations in deployed LLMs?
  23. Explain safety measures for generative AI in production.
  24. How do you research state-of-the-art generative AI techniques?
  25. What are future trends in generative AI research and deployment?
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#LLM

#LLM
Level Topic Subtopics
Basic Introduction to LLMs What is an LLM, History of LLMs, Applications of LLMs, Types of LLMs, Difference from traditional ML models
NLP Fundamentals Tokenization, Embeddings, Language Modeling, Contextual Representations, Sequence-to-Sequence Tasks
AI & ML Basics Neural Networks, Transformers Overview, Supervised vs Unsupervised Learning, Gradient Descent, Loss Functions
Tools & Frameworks Python, PyTorch, TensorFlow, Hugging Face Transformers, OpenAI API, Google Colab
Ethics & Safety Bias in LLMs, Model Hallucinations, Responsible AI, Privacy Concerns, Misinformation Risk
Intermediate Transformer Architecture Attention Mechanism, Self-Attention, Multi-Head Attention, Positional Encoding, Encoder-Decoder Models
Tokenization & Vocabulary Subword Tokenization, Byte-Pair Encoding (BPE), SentencePiece, Vocabulary Size, Token Embeddings
Pretraining & Fine-Tuning Pretraining Objectives, Masked Language Modeling, Causal Language Modeling, Fine-Tuning Strategies, Transfer Learning
Prompt Engineering Prompt Design, Few-Shot Prompts, Zero-Shot Prompts, Chain-of-Thought Prompts, Context Window Management
Evaluation & Metrics Perplexity, BLEU, ROUGE, Accuracy, Human Evaluation, Model Benchmarking
Advanced Scaling LLMs Model Parallelism, Data Parallelism, Parameter Efficient Fine-Tuning, Mixed Precision Training, Large Dataset Management
Advanced Architectures GPT, BERT, T5, LLaMA, Falcon, Diffusion-Augmented LLMs, Multi-Modal Transformers
Multi-Modal LLMs Text-to-Image, Text-to-Audio, Text-to-Video, Cross-Modal Embeddings, Fusion Techniques
Optimization & Regularization Gradient Clipping, Learning Rate Schedulers, Dropout, Layer Normalization, Activation Functions
LLM Deployment & Serving Model Serving, APIs, Latency Optimization, Cloud Deployment, Monitoring & Logging
Expert Cutting-Edge LLM Techniques RLHF (Reinforcement Learning with Human Feedback), Instruction Tuning, Self-Supervised Learning, Retrieval-Augmented Generation (RAG), LLM Alignment
Safety & Governance Mitigating Bias, Detecting Hallucinations, Ethical Considerations, Responsible Deployment, Model Auditing
Research & Innovation Open-Source LLMs, Continual Learning, Few-Shot and Zero-Shot Learning, Large-Scale Pretraining, Emerging Architectures
Explainability & Interpretability SHAP, LIME, Counterfactual Explanations, Attention Visualization, Understanding Latent Representations
Performance & Efficiency Memory Optimization, Quantization, Pruning, Inference Acceleration, Energy-Efficient LLMs

1. LLM Basics

  1. What is a Large Language Model (LLM)?
  2. How do LLMs differ from traditional machine learning models?
  3. What are common applications of LLMs?
  4. Explain the evolution of LLMs over the years.
  5. Difference between supervised, unsupervised, and self-supervised learning.
  6. Explain tokenization and why it is important.
  7. What are embeddings and their role in LLMs?
  8. Difference between contextual and non-contextual embeddings.
  9. Explain the concept of a context window.
  10. What is language modeling?
  11. What is zero-shot learning?
  12. What is few-shot learning?
  13. Explain one-shot learning.
  14. How do LLMs handle long-term dependencies in text?
  15. What is model hallucination in LLMs?
  16. Explain overfitting and underfitting in LLMs.
  17. How is evaluation performed for LLMs?
  18. Explain perplexity as a metric.
  19. What are common LLM frameworks and tools?
  20. Explain fine-tuning vs pretraining.
  21. Difference between deterministic and probabilistic LLM outputs.
  22. Explain multi-turn conversation handling.
  23. What is prompt engineering?
  24. Difference between narrow AI and LLM capabilities.
  25. Explain ethical considerations in LLM usage.

2. Intermediate LLM Concepts

  1. Explain transformer architecture.
  2. What is self-attention and how does it work?
  3. Difference between encoder, decoder, and encoder-decoder architectures.
  4. Explain multi-head attention.
  5. What is positional encoding?
  6. Difference between GPT, BERT, and T5 models.
  7. Explain tokenization strategies: BPE, SentencePiece.
  8. How does vocabulary size affect LLM performance?
  9. Explain embedding representations in LLMs.
  10. What is instruction tuning?
  11. Difference between fine-tuning and parameter-efficient fine-tuning (PEFT).
  12. Explain chain-of-thought prompting.
  13. Difference between few-shot, zero-shot, and one-shot prompting.
  14. How do LLMs handle out-of-vocabulary (OOV) words?
  15. Explain beam search in text generation.
  16. What is temperature in text generation?
  17. How do LLMs mitigate repetition in outputs?
  18. Explain sequence-to-sequence generation.
  19. Difference between dense and sparse attention.
  20. How do embeddings help in retrieval-augmented generation (RAG)?
  21. Explain model evaluation metrics for LLMs.
  22. How do you monitor LLM performance?
  23. Explain qualitative evaluation techniques.
  24. How do LLMs handle multi-modal inputs?
  25. Explain common challenges in intermediate LLM models.

3. Advanced LLM Topics

  1. How do you scale LLMs to billions of parameters?
  2. Explain model parallelism and data parallelism.
  3. What is mixed precision training?
  4. How do you optimize memory usage during training?
  5. How do you improve inference speed for large LLMs?
  6. Explain multi-modal LLMs (text-to-image, text-to-audio).
  7. What is reinforcement learning with human feedback (RLHF)?
  8. How do LLMs handle retrieval-augmented generation (RAG)?
  9. Explain large-scale pretraining techniques.
  10. How do you implement LoRA (Low-Rank Adaptation)?
  11. Difference between GPT-style and BERT-style LLMs.
  12. How do you prevent hallucinations in LLM outputs?
  13. Explain training instability issues in LLMs.
  14. How do you handle catastrophic forgetting?
  15. Explain instruction-tuned LLMs.
  16. How do LLMs manage ambiguous queries?
  17. Explain attention visualization techniques.
  18. How do you debug LLM outputs?
  19. Explain evaluation of multi-turn dialogues.
  20. How do LLMs handle low-resource languages?
  21. Explain scaling laws for LLMs.
  22. How do you combine retrieval and generation efficiently?
  23. Explain cross-lingual and multilingual LLMs.
  24. How do you monitor LLM performance in production?
  25. How do you defend against prompt injection attacks?

4. Expert-Level LLM Concepts

  1. Explain state-of-the-art LLM architectures (GPT-4/5, LLaMA, Falcon).
  2. How do you align LLM outputs with human intent?
  3. Explain safety measures for harmful outputs.
  4. How do you implement continual learning in LLMs?
  5. Explain scaling LLMs with trillions of parameters.
  6. How do LLMs handle context beyond the context window?
  7. Explain retrieval-augmented generation at industrial scale.
  8. How do you ensure privacy-preserving LLM deployment?
  9. Explain federated learning for LLMs.
  10. How do you detect and mitigate bias in LLMs?
  11. Explain interpretability techniques (SHAP, LIME).
  12. How do you monitor hallucinations in deployed LLMs?
  13. Explain AI governance and regulatory compliance for LLMs.
  14. How do you combine LLMs with knowledge graphs?
  15. Explain multi-modal foundation models.
  16. How do you perform model distillation for LLMs?
  17. How do you optimize energy and compute efficiency?
  18. Explain retrieval-augmented generation with embeddings.
  19. How do you benchmark LLMs across multiple tasks?
  20. How do you handle instruction-following vs creative generation trade-offs?
  21. Explain multi-agent LLM systems.
  22. How do you debug large-scale LLM training pipelines?
  23. How do you implement RLHF at industrial scale?
  24. Explain evaluation metrics for open-ended generation tasks.
  25. What are emerging trends in LLM research and deployment?
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#AI

#AI
Level Topic Subtopics
Basic Introduction to AI What is AI, History of AI, Applications of AI, Types of AI (Narrow, General, Super AI), AI vs Human Intelligence
AI Techniques & Concepts Problem Solving, Search Techniques, Heuristics, Knowledge Representation, Reasoning
Machine Learning Basics Supervised Learning, Unsupervised Learning, Reinforcement Learning, Regression, Classification
AI Tools & Libraries Python for AI, Numpy, Pandas, Scikit-learn, Matplotlib, TensorFlow Basics
AI Ethics & Society AI Ethics, Bias in AI, Responsible AI, AI in Society, Limitations of AI
Intermediate Machine Learning Algorithms Decision Trees, Random Forest, K-Nearest Neighbors, Support Vector Machines, Naive Bayes
Neural Networks Perceptron, Feedforward Neural Network, Backpropagation, Activation Functions, Gradient Descent
Natural Language Processing Text Preprocessing, Tokenization, Lemmatization, Stopwords, Bag-of-Words, TF-IDF
Reinforcement Learning Markov Decision Processes, Q-Learning, Policy & Value Functions, Exploration vs Exploitation
AI Model Evaluation Confusion Matrix, Accuracy, Precision, Recall, F1 Score, ROC-AUC
Advanced Deep Learning Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), LSTM, GRU, Autoencoders
Computer Vision Image Classification, Object Detection, Image Segmentation, OpenCV, Pretrained Models
Advanced NLP Word Embeddings, Word2Vec, GloVe, Transformer Models, BERT, GPT
Optimization Techniques Gradient Descent Variants, Regularization, Dropout, Hyperparameter Tuning, Early Stopping
AI Pipelines & Deployment Model Deployment, API Creation, Model Versioning, Monitoring, MLOps Basics
Expert Generative AI GANs, Variational Autoencoders, Diffusion Models, Text-to-Image Generation, DeepFakes
Advanced Reinforcement Learning Deep Q-Networks, Policy Gradient Methods, Actor-Critic Models, Multi-Agent RL
Explainable AI (XAI) SHAP, LIME, Interpretability Techniques, Model Transparency, Trust in AI
AI Research & Trends Large Language Models, Self-Supervised Learning, Few-Shot Learning, AI Alignment, Quantum AI
AI Ethics & Governance AI Regulation, AI Accountability, Fairness Metrics, Privacy-Preserving AI, AI Risk Management

1. Introduction to AI

  1. What is Artificial Intelligence?
  2. Differentiate between ANI, AGI, and ASI.
  3. List real-world applications of AI.
  4. What are the main goals of AI?
  5. How does AI differ from traditional programming?
  6. What is the Turing Test?
  7. Define intelligent agents in AI.
  8. Explain rationality in AI systems.
  9. What is the role of search algorithms in AI?
  10. Define heuristic in AI context.
  11. Difference between strong AI and weak AI.
  12. What is knowledge representation?
  13. Explain ontology in AI.
  14. Difference between deterministic and stochastic environments.
  15. Explain adversarial search with examples.
  16. What is game theory in AI?
  17. Explain production systems.
  18. Define utility-based agents.
  19. What is the frame problem in AI?
  20. Explain the concept of AI winter.
  21. What is fuzzy logic in AI?
  22. Define expert systems with examples.
  23. What is the difference between AI and Machine Learning?
  24. Explain natural intelligence vs artificial intelligence.
  25. What are the challenges in AI adoption?

2. Machine Learning

  1. Define machine learning.
  2. Types of machine learning: supervised, unsupervised, reinforcement.
  3. Difference between classification and regression.
  4. What is overfitting?
  5. Explain underfitting.
  6. What is bias-variance tradeoff?
  7. Define cross-validation.
  8. Difference between training and testing dataset.
  9. Explain confusion matrix.
  10. What is precision and recall?
  11. Define ROC curve and AUC.
  12. Explain clustering with examples.
  13. What is dimensionality reduction?
  14. PCA vs LDA -- difference.
  15. Define feature engineering.
  16. Explain ensemble learning.
  17. Bagging vs Boosting -- difference.
  18. Explain Random Forest algorithm.
  19. Explain Support Vector Machines (SVM).
  20. Define k-nearest neighbors (KNN).
  21. Difference between parametric and non-parametric models.
  22. What is gradient descent?
  23. Difference between batch and stochastic gradient descent.
  24. What is a cost function?
  25. Explain reinforcement learning in detail.

3. Deep Learning

  1. Define deep learning.
  2. Difference between AI, ML, and DL.
  3. Explain artificial neural networks.
  4. What is backpropagation?
  5. Role of activation functions in neural networks.
  6. Explain sigmoid, ReLU, and tanh functions.
  7. What is dropout in neural networks?
  8. Define vanishing and exploding gradient problems.
  9. What is gradient clipping?
  10. Difference between CNN and RNN.
  11. Explain convolution operation in CNNs.
  12. What are pooling layers?
  13. Explain LSTM architecture.
  14. GRU vs LSTM difference.
  15. Define attention mechanism.
  16. What is transfer learning?
  17. Explain fine-tuning in deep learning.
  18. What is batch normalization?
  19. Define optimizer -- SGD, Adam, RMSProp.
  20. Explain cost functions in neural networks.
  21. What is autoencoder?
  22. Define generative adversarial networks (GANs).
  23. Explain diffusion models.
  24. What is reinforcement learning with deep networks (Deep Q-Network)?
  25. Challenges in deep learning implementation.

4. Natural Language Processing (NLP)

  1. What is NLP?
  2. Explain tokenization.
  3. Difference between stemming and lemmatization.
  4. Define POS tagging.
  5. Explain n-grams in NLP.
  6. What is bag-of-words model?
  7. TF-IDF -- explain its use.
  8. Define word embeddings.
  9. Difference between Word2Vec and GloVe.
  10. Explain sequence-to-sequence models.
  11. What is machine translation in NLP?
  12. Define sentiment analysis.
  13. What are named entity recognitions (NER)?
  14. Explain text summarization techniques.
  15. What is speech recognition?
  16. Explain conversational AI and chatbots.
  17. Define transformer architecture.
  18. What is BERT?
  19. What is GPT and how does it work?
  20. Explain RNN in NLP context.
  21. Difference between CNN and RNN in NLP tasks.
  22. What is semantic search?
  23. Explain retrieval-augmented generation (RAG).
  24. What is prompt engineering?
  25. Challenges in NLP.

5. AI Ethics & Safety

  1. What is AI bias?
  2. Define fairness in AI.
  3. Explain transparency in AI models.
  4. What is explainable AI?
  5. What is SHAP and LIME in interpretability?
  6. Define ethical AI principles.
  7. What is responsible AI?
  8. Explain GDPR's impact on AI.
  9. What are risks of AI misuse?
  10. Define AI governance.
  11. Explain accountability in AI systems.
  12. What is adversarial attack in AI?
  13. Difference between black-box and white-box attacks.
  14. Define robustness in AI systems.
  15. What are AI hallucinations in LLMs?
  16. Define data privacy in AI.
  17. Explain ethical concerns of autonomous vehicles.
  18. What is algorithmic discrimination?
  19. What is AI safety research?
  20. Explain human-in-the-loop in AI systems.
  21. What is trustworthy AI?
  22. Role of audits in AI ethics.
  23. Challenges in AI regulation.
  24. What is AI alignment problem?
  25. Future scope of responsible AI.
   Machine Learning       LLM       GenAI       Deep Learning       NLP   
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24 June 2025

#NLP

#NLP
Level Topic Subtopics
Basic Introduction to NLP What is NLP, History of NLP, Applications, Challenges, NLP vs Computational Linguistics
Text Preprocessing Tokenization, Stopwords Removal, Stemming, Lemmatization, Text Cleaning
Basic NLP Tasks Language Modeling, Text Classification, Sentiment Analysis, Named Entity Recognition (NER), Part-of-Speech Tagging
NLP Tools & Libraries NLTK, SpaCy, Gensim, Hugging Face Transformers, Python NLP Libraries
Evaluation Metrics Accuracy, Precision, Recall, F1 Score, BLEU Score, ROUGE, Perplexity
Intermediate Embeddings & Representations Word Embeddings (Word2Vec, GloVe), Contextual Embeddings, TF-IDF, Bag-of-Words, One-Hot Encoding
Sequence Models Recurrent Neural Networks (RNN), LSTM, GRU, Sequence-to-Sequence Models, Attention Mechanism
Advanced NLP Tasks Machine Translation, Question Answering, Text Summarization, Speech-to-Text, Text-to-Speech
NLP Pipelines & Workflow Preprocessing Pipelines, Tokenization Strategies, Model Training, Evaluation, Deployment
Information Retrieval & Search Vector Space Models, BM25, Embedding-based Retrieval, Semantic Search, Ranking Metrics
Advanced Transformer Models Transformers Architecture, BERT, GPT, RoBERTa, T5, Encoder-Decoder Models, Attention Mechanism
Contextual & Large Language Models Contextual Embeddings, Pretrained Language Models, Fine-Tuning, Prompt Engineering, Few-Shot/Zero-Shot Learning
NLP for Multi-Modal Tasks Text-to-Image, Text-to-Audio, Vision-Language Models, Cross-Modal Learning, Multi-Modal Transformers
Optimization & Training Learning Rate Schedulers, Gradient Clipping, Regularization, Mixed Precision, Transfer Learning
Advanced Evaluation Perplexity, BLEU, ROUGE, METEOR, Human Evaluation, Error Analysis, Bias Detection
Expert Generative NLP Language Generation, Chatbots, GPT-style Models, Reinforcement Learning with Human Feedback (RLHF), Dialogue Systems
Explainability & Interpretability Attention Visualization, SHAP, LIME, Counterfactual Explanations, Model Debugging
NLP Deployment & MLOps Serving NLP Models, API Integration, Cloud Deployment, Model Monitoring, Continuous Learning
Research & Emerging Trends Self-Supervised Learning, Few-Shot / Zero-Shot NLP, Multi-Lingual Models, Retrieval-Augmented Generation, Foundation Models
Ethics & Responsible AI Bias & Fairness, Toxicity Detection, Data Privacy, Ethical NLP, Safe AI Deployment

1. NLP Basics

  1. What is Natural Language Processing (NLP)?
  2. Difference between NLP and computational linguistics.
  3. What are some real-world applications of NLP?
  4. Explain tokenization and its types.
  5. What is stemming and lemmatization?
  6. Difference between stemming and lemmatization.
  7. What are stopwords and why are they removed?
  8. Explain text cleaning techniques.
  9. Difference between bag-of-words and TF-IDF.
  10. What is part-of-speech tagging?
  11. Explain named entity recognition (NER).
  12. What is sentiment analysis?
  13. Explain language modeling.
  14. Difference between unigram, bigram, and n-gram models.
  15. What are word embeddings?
  16. Explain evaluation metrics: accuracy, precision, recall, F1 score.
  17. Difference between supervised and unsupervised NLP tasks.
  18. What is perplexity in language models?
  19. Difference between lemmatization and normalization.
  20. What are common NLP datasets?
  21. Explain stopword removal trade-offs.
  22. What is text classification?
  23. Explain similarity measures in NLP (cosine, Jaccard).
  24. What are common Python NLP libraries?
  25. How does NLP handle noisy or unstructured text?

2. Intermediate NLP

  1. Explain Word2Vec embeddings.
  2. What is GloVe?
  3. Difference between Word2Vec and GloVe.
  4. Explain context-free embeddings vs contextual embeddings.
  5. What is a recurrent neural network (RNN)?
  6. Difference between RNN, LSTM, and GRU.
  7. Explain sequence-to-sequence (seq2seq) models.
  8. What is attention mechanism?
  9. Explain machine translation using NLP.
  10. How do you perform text summarization?
  11. Explain question answering systems.
  12. What is speech-to-text and text-to-speech in NLP?
  13. How do you perform semantic search?
  14. Explain information retrieval models (vector space, BM25).
  15. What is embedding-based retrieval?
  16. How do you handle multi-lingual NLP tasks?
  17. Explain preprocessing pipelines in NLP.
  18. What is entity linking?
  19. How do you evaluate NER systems?
  20. Explain evaluation metrics for text generation (BLEU, ROUGE).
  21. How do you handle out-of-vocabulary (OOV) words?
  22. Explain sentence embeddings.
  23. Difference between feature-based and fine-tuning approaches for embeddings.
  24. How do you perform text clustering?
  25. Explain topic modeling (LDA, NMF).

3. Advanced NLP

  1. Explain transformer architecture.
  2. Difference between encoder, decoder, and encoder-decoder models.
  3. Explain BERT architecture.
  4. What is GPT architecture?
  5. Difference between BERT and GPT.
  6. Explain RoBERTa and T5 models.
  7. How do you fine-tune pre-trained language models?
  8. Explain prompt engineering.
  9. Difference between few-shot, one-shot, and zero-shot learning.
  10. What is retrieval-augmented generation (RAG)?
  11. Explain multi-modal NLP (text-to-image, text-to-audio).
  12. How do you implement cross-modal learning?
  13. Explain transfer learning in NLP.
  14. How do you perform regularization in NLP models?
  15. Explain gradient clipping in transformer training.
  16. Difference between masked language modeling (MLM) and causal language modeling.
  17. How do you prevent hallucinations in text generation?
  18. Explain evaluation metrics for multi-turn dialogue systems.
  19. How do you handle long sequences in transformers?
  20. Explain multi-head attention and positional encoding.
  21. How do you optimize transformer models for inference?
  22. What are embedding spaces and similarity measures?
  23. Explain knowledge distillation in NLP.
  24. How do you handle bias in NLP models?
  25. Explain human evaluation methods for NLP outputs.

4. Expert-Level NLP

  1. Explain generative NLP models.
  2. How do GPT-style models generate text?
  3. What is reinforcement learning with human feedback (RLHF)?
  4. Explain dialogue systems and chatbots.
  5. How do you align LLMs with human intent?
  6. Explain foundation models in NLP.
  7. How do you deploy NLP models in production?
  8. What are best practices for API-based NLP services?
  9. Explain continuous learning for NLP systems.
  10. How do you monitor deployed NLP models?
  11. Explain model drift and data drift.
  12. How do you handle multi-lingual and cross-lingual NLP at scale?
  13. Explain retrieval-augmented generation at industrial scale.
  14. How do you implement privacy-preserving NLP?
  15. Explain federated learning in NLP.
  16. How do you interpret large language models (LLMs)?
  17. Explain SHAP and LIME for NLP models.
  18. How do you measure fairness and mitigate bias in NLP?
  19. Explain safe and responsible deployment of generative NLP models.
  20. How do you combine knowledge graphs with NLP models?
  21. Explain multi-agent NLP systems.
  22. How do you debug large-scale NLP training pipelines?
  23. Explain evaluation metrics for open-ended tasks.
  24. What are emerging trends in NLP research?
  25. How do you combine retrieval and generation for advanced NLP applications?
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