deep learning

Generic synopsis: This post compiles essential AI interview questions across ML, deep learning, NLP, and MLOps, covering fundamentals (bias-variance, overfitting, metrics), advanced topics (transformers, attention, LLMs, prompt engineering), system design, data handling, ethics, and deployment. Includes sample answers, tips, and common pitfalls.

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An actionable roadmap for studying AI: set clear goals; review math fundamentals (linear algebra, calculus, probability, statistics); learn Python and data handling; cover ML/DL algorithms, model evaluation, and MLOps; explore NLP and computer vision; practice with projects and datasets; mind ethics and bias; use key tools; build a portfolio; track progress with a repeatable study plan.

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