What is k-Fold Cross-Validation?
What is the difference between supervised and unsupervised learning?
What is reinforcement learning? What is it used for?
Definition: Learning paradigm where agent learns to make decisions by interacting with environment and receiving rewards/penalties
Key components: Agent, environment, state, action, reward signal - agent learns optimal policy to maximize cumulative reward
Game AI: Playing chess, Go, video games (AlphaGo, OpenAI Dota 2) - learning winning strategies through self-play
Robotics & Control: Robot navigation, autonomous vehicles, resource management, optimizing complex sequential decisions
Business applications: Recommendation systems, trading strategies, resource allocation, dynamic pricing
What is data/class imbalance? What consequences does it have? How to treat those?
Definition: When classes in dataset are not equally represented (e.g., 95% negative, 5% positive cases)
Consequences: Model biased toward majority class, poor performance on minority class, misleading accuracy metrics
Resampling: Oversampling minority class (SMOTE), undersampling majority class, or combination of both
Algorithm techniques: Class weights, cost-sensitive learning, ensemble methods (balanced random forest)
Better metrics: Use precision, recall, F1-score, ROC-AUC instead of accuracy for imbalanced datasets
What is feature engineering?
Definition: Process of creating, transforming, and selecting features from raw data to improve model performance
Techniques: Creating new features (interactions, polynomials), transforming existing ones (scaling, encoding), extracting from text/dates
Importance: Often more impactful than algorithm choice, requires domain knowledge, can significantly boost model accuracy
Examples: One-hot encoding categorical variables, extracting day/month from dates, creating ratio features, binning continuous variables
What are transformer-based models?
Architecture: Neural network architecture based on self-attention mechanism, processes sequences without recurrence (unlike RNN/LSTM)
Self-attention: Allows model to weigh importance of different words in sequence, capturing long-range dependencies efficiently
Examples: BERT, GPT (GPT-3, GPT-4), T5, transformers power most modern NLP systems
Advantages: Parallel processing (faster training), better long-range context, transfer learning capabilities
Applications: Translation, text generation, question answering, summarization, increasingly used beyond NLP (vision transformers)
What is RAG (retrieval-augmented generation) and how does it relate to your expertise?
Definition: Technique combining retrieval systems with generative models - retrieves relevant documents then generates response based on retrieved context
Components: Vector database/search engine for retrieval, embeddings for similarity search, LLM for generation
Advantages: Reduces hallucinations, grounds responses in real data, can access up-to-date information beyond training cutoff
Use cases: Enterprise Q&A systems, chatbots with knowledge bases, document analysis, customer support automation
Technical implementation: Involves chunking documents, creating embeddings, storing in vector DB (Pinecone, Weaviate), semantic search + prompt engineering
Personal expertise: Experience building RAG systems, working with vector databases, optimizing retrieval quality, prompt engineering for better generation
How is fine-tuning different from regular training?
How do the fine-tuning options and limitations differ between closed-source models (e.g., OpenAI GPT-4) and open-source models that you can self-host?
Closed-source (OpenAI): Limited to API-based fine-tuning, no access to model weights, restricted customization through provided interfaces only
Open-source: Full control over model architecture, can modify any layer, complete fine-tuning flexibility, access to all weights
Cost considerations: Closed-source has per-token costs, open-source requires infrastructure investment but no usage fees
Data privacy: Closed-source sends data to external servers, open-source allows complete on-premise deployment for sensitive data
Technical expertise: Closed-source easier to use with less ML knowledge required, open-source needs deeper understanding of training, hardware, optimization
Examples: Open-source options include Llama 2, Mistral, Falcon - can use LoRA, QLoRA, full fine-tuning with complete control
What parameter-efficient fine-tuning techniques exist for open models (e.g., LoRA, QLoRA), and how do they work conceptually?
LoRA (Low-Rank Adaptation): Freezes pre-trained weights, adds small trainable rank decomposition matrices to layers, drastically reduces trainable parameters
How LoRA works: Instead of updating weight matrix W, trains two smaller matrices A and B where W_new = W + AB, A and B much smaller
QLoRA: Combines LoRA with quantization, loads model in 4-bit precision, trains LoRA adapters in higher precision, enables fine-tuning on consumer GPUs
Memory benefits: Can fine-tune 65B parameter models on single GPU, reduces memory from hundreds of GB to tens of GB
Other techniques: Prefix tuning (add trainable tokens), adapter layers (insert small modules), prompt tuning (optimize soft prompts)
Practical benefits: Faster training, lower costs, multiple task-specific adapters can share base model, easier deployment and version control
List vs Tuple in Python? When do you use what?
List: Mutable (can modify after creation), uses square brackets [], slower, for collections that change
Tuple: Immutable (cannot modify after creation), uses parentheses (), faster, less memory, for fixed collections
Use list when: Data will be modified (append, remove, sort), working with dynamic collections, need list methods
Use tuple when: Data should not change (coordinates, RGB values), dictionary keys, function return multiple values, better performance needed
What are decorators in Python?
Definition: Functions that modify behavior of other functions/methods, using @decorator_name syntax above function definition
How they work: Take function as input, add functionality, return modified function - implements wrapper pattern
Common uses: Logging, timing functions, authentication/authorization, caching (memoization), validation
Built-in examples: @property, @staticmethod, @classmethod, @lru_cache for performance optimization
Practical benefit: Clean code separation of concerns, reusable cross-cutting functionality, don't repeat yourself (DRY)
What is the purpose of the .groupby() method in the Pandas library?
Explain the difference between a generator and a normal function that returns a list. Why are generators (which use the yield keyword) particularly beneficial when processing very large, potentially memory-intensive datasets in data science?
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