A complete, textbook-level explanation of the Transformer architecture from first principles. Covers self-attention, positional encoding, scaling laws, and implementation-written like a university textbook chapter.
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Complete comparison of bagging, boosting, and stacking. Covers Random Forest, AdaBoost, Gradient Boosting, and XGBoost, bootstrap sampling, the bias-variance tradeoff, and guidance on which ensemble method to use and when
A definitive step-by-step guide from zero ML knowledge to professional-grade machine learning engineer. Includes realistic 12–18 month timeline, mathematics foundations, Python programming, deep learning, specializations, production ML, and portfolio strategy. Written by an independent AI researcher.
A field-tested, end-to-end machine learning interview guide covering every round - algorithms coding, ML coding, deep learning theory, transformer and LLM questions, reinforcement learning, system design, behavioural interviews, offer negotiation, and decision-making. Written from firsthand experience interviewing across multiple frontier AI labs, with every technical claim backed by a primary source.
A complete, textbook-grade guide to SHAP value analysis-Shapley game theory, the exact formula worked by hand, the four axioms, KernelSHAP vs TreeSHAP internals, additivity and interaction values, and a full shap Python walkthrough for explaining any machine learning model.
