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portada The Complete AI Engineer — Volume 1: Foundations & Classical Machine Learning: Python Internals, Mathematical Rigor & Classical Machine Learning from Scratch
Type
Physical Book
Publisher
Author
Language
English
Pages
370
Format
Paperback
ISBN13
9798178899670

The Complete AI Engineer — Volume 1: Foundations & Classical Machine Learning: Python Internals, Mathematical Rigor & Classical Machine Learning from Scratch

Awasthi, Ram (Author) · Independently published · Paperback

The Complete AI Engineer — Volume 1: Foundations & Classical Machine Learning: Python Internals, Mathematical Rigor & Classical Machine Learning from Scratch - Awasthi, Ram

New Book Imported to Taiwan
Delivery: 12 Nov - 20 Nov Shipping: 6 to 7 business days.
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NT$ 1,283

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Synopsis "The Complete AI Engineer — Volume 1: Foundations & Classical Machine Learning: Python Internals, Mathematical Rigor & Classical Machine Learning from Scratch"

Master the Mathematical & Computational Bedrock of Artificial Intelligence Before you can train frontier foundation models or architect industrial AI infrastructure, you must master the fundamental forces of computation, linear algebra, and statistical learning. Volume 1 of The Complete AI Engineer delivers the uncompromising, first-principles curriculum demanded of world-class AI engineers. Every algebraic derivation, matrix factorization, and algorithm trace is written in full directly on the page. No hand-waving. No hidden libraries. Just raw, foundational mastery. What You Will Master in Volume 1: Part 0: The AI Operating System — CPython object memory allocation, reference counting, GIL mechanics, NumPy strided memory layouts, and high-performance Polars/Arrow data lakehouses. Part 1: Mathematics for AI Engineers — Geometric SVD, Autograd vector-Jacobian products (VJPs), Bayesian loss bridges, Shannon entropy, and KKT duality constrained optimization. Part 2: Classical Machine Learning from Scratch — Closed-form OLS normal equations, Newton-Raphson logistic regression, gradient boosted decision trees (XGBoost & CatBoost), soft-margin SVMs, and UMAP manifold projection. Structuring ML Projects — Avoidable bias, Bayes optimal error, and quantitative error analysis spreadsheets. Reinforcement Learning & Time Series — Bellman equations, deep Q-networks, DeepAR probabilistic forecasting, and Google Temporal Fusion Transformers. 3 Production Capstones & 30+ Solved FAANG Interview Proofs with full step-by-step mathematical solutions. Whether you are a software engineer transitioning into AI or an experienced researcher demanding complete mathematical rigor, Volume 1 is your definitive blueprint.

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