100+ LLM Interview Questions and Answers (GitHub Repo)

Anyone preparing for #AI/#ML Interviews, it is mandatory to have good knowledge related to #LLM topics.

This# repo includes 100+ LLM interview questions (with answers) spanning over LLM topics like
LLM Inference
LLM Fine-Tuning
LLM Architectures
LLM Pretraining
Prompt Engineering
etc.

🖕 Github Repo - https://github.com/KalyanKS-NLP/LLM-Interview-Questions-and-Answers-Hub

https://news.1rj.ru/str/DataScienceM ✅

🚀 Top 9 Predictive Models Every Data Scientist Should Know in 2025

In the world of Machine Learning, selecting the right predictive model is crucial for solving real-world problems effectively.

Here’s a deep dive into the top 9 models and when to use them :-

1️⃣ Regularized Linear/Logistic Regression

• Best for: Tabular data with mostly linear effects
• Why: Fast, interpretable, strong baseline
• Watch out: Multicollinearity, feature scaling
• Key knobs: L1/L2/Elastic Net strength

2️⃣ Decision Trees

• Best for: Simple rules and quick interpretability
• Why: Captures nonlinearity and feature interactions
• Watch out: Overfitting
• Key knobs: max_depth, min_samples_leaf

3️⃣ Random Forest

• Best for: Mixed-type tabular data
• Why: Robust, handles missingness, low tuning effort
• Watch out: Slower inference for large models
• Key knobs: n_estimators, max_features

4️⃣ Gradient Boosting Trees

• Best for: Structured data requiring top performance
• Why: Handles complex patterns and interactions
• Watch out: Overfitting if not tuned carefully
• Key knobs: learning_rate, n_estimators, max_depth, regularization

5️⃣ Support Vector Machines (linear/RBF)

• Best for: Medium-sized datasets with clear margins
• Why: Strong performance after scaling
• Watch out: Kernel choice and cost at scale
• Key knobs: C, kernel, gamma

6️⃣ k-Nearest Neighbors (k-NN)

• Best for: Small datasets with local structure
• Why: Simple, non-parametric
• Watch out: Poor scaling, sensitive to feature scaling
• Key knobs: k, distance metric, weighting

7️⃣ Naive Bayes

• Best for: High-dimensional sparse features (like text)
• Why: Very fast, competitive for many applications
• Watch out: Independence assumption
• Key knobs: smoothing (alpha)

8️⃣ Multilayer Perceptrons (Feedforward Neural Networks)

• Best for: Nonlinear relationships with sufficient data & compute
• Why: Flexible universal approximators
• Watch out: Tuning, overfitting without regularization
• Key knobs: layers/neurons, dropout, learning rate

9️⃣ Classical Time-Series Models

• Best for: Univariate or small-multivariate forecasting with seasonality
• Why: Transparent baselines, good for limited data
• Watch out: Stationarity, careful feature engineering
• Key knobs: (p, d, q), seasonal terms, exogenous variables

💡 Pro Tip: Each model has its strengths and trade-offs. Understanding when to use which model and how to tune its hyperparameters is key to building robust and interpretable predictive systems.

https://news.1rj.ru/str/DataScienceM ✅

🚀Stanford just completed a must-watch for anyone serious about AI:

🎓 “𝗖𝗠𝗘 𝟮𝟵𝟱: 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 & 𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀” is now live entirely on YouTube and it’s pure gold.

If you’re building your AI career, stop scrolling.
This isn’t another surface-level overview. It’s the clearest, most structured intro to LLMs you could follow, straight from the Stanford Autumn 2025 curriculum.

📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲:
• How Transformers actually work (tokenization, attention, embeddings)
• Decoding strategies & MoEs
• LLM finetuning (LoRA, RLHF, supervised)
• Evaluation techniques (LLM-as-a-judge)
• Optimization tricks (RoPE, quantization, approximations)
• Reasoning & scaling
• Agentic workflows (RAG, tool calling)

🧠 My workflow: I usually take the trannoscripts, feed them into NotebookLM, and once I’ve done the lectures, I replay them during walks or commutes. That combo works wonders for retention.

🎥 Watch these now:

- Lecture 1: https://lnkd.in/dDER-qyp
- Lecture 2: https://lnkd.in/dk-tGUDm
- Lecture 3: https://lnkd.in/drAPdjJY
- Lecture 4: https://lnkd.in/e_RSgMz7
- Lecture 5: https://lnkd.in/eivMA9pe
- Lecture 6: https://lnkd.in/eYwwwMXn
- Lecture 7: https://lnkd.in/eKwkEDXV
- Lecture 8: https://lnkd.in/eEWvyfyK
- Lecture 9: https://lnkd.in/euiKRGaQ

🗓 Do yourself a favor for this 2026: block 2-3 hours per week / llectue and go through them.

If you’re in AI — whether building infra, agents, or apps — this is the foundational course you don’t want to miss.

Let’s level up.
https://news.1rj.ru/str/CodeProgrammer 😅