Master the Mathematics Driving Today's AI Revolution Whether you're a data-driven professional, graduate student, or ambitious self-learner, this comprehensive textbook propels you from foundational principles to cutting-edge theory-one carefully crafted lesson at a time. Why You'll Love This Book Complete Coverage - 44 focused chapters span vector spaces, probability theory, information measures, optimization, learning theory, and reinforcement learning. 500+ Practice Problems - Sharpen your intuition with a ...
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Master the Mathematics Driving Today's AI Revolution Whether you're a data-driven professional, graduate student, or ambitious self-learner, this comprehensive textbook propels you from foundational principles to cutting-edge theory-one carefully crafted lesson at a time. Why You'll Love This Book Complete Coverage - 44 focused chapters span vector spaces, probability theory, information measures, optimization, learning theory, and reinforcement learning. 500+ Practice Problems - Sharpen your intuition with a massive bank of exercises designed to mirror real-world AI challenges. Step-by-Step Solutions - Every problem includes a fully worked solution, turning frustration into "aha!" moments and ensuring true mastery. Built for Self-Study - Clear explanations, logical progression, and concise notation let you learn at your pace-ideal for remote learners and busy professionals. Exam & Interview Ready - Solidify the mathematical fundamentals that top tech companies and graduate programs expect you to know cold. Future-Proof Skills - Gain the rigorous toolkit required for deep learning research, probabilistic modeling, and advanced reinforcement learning. Inside You'll Discover Vector, matrix, and tensor algebra tailored to representation learning Measure-theoretic probability and Bayesian inference for uncertainty modeling Information-theoretic loss functions and divergence measures powering modern objectives Capacity control via VC dimension, PAC-Bayes, and Rademacher complexity Convex and non-convex optimization theory behind gradient-based training Random matrix insights into high-dimensional behavior Regret bounds, bandit theory, and Bellman fixed points for sequential decision making Who Should Read This Book? Machine learning engineers seeking deeper mathematical fluency Graduate students preparing for qualifying exams Researchers bridging theory and practical model design Data scientists transitioning from code-first to math-first understanding Join thousands of learners who have transformed their AI careers by mastering the mathematics that matter. Equip yourself today and turn complex algorithms into crystal-clear logic you can explain, improve, and innovate upon.
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Add this copy of Artificial Intelligence Mathematics: The All in One to cart. $32.20, new condition, Sold by Ingram Customer Returns Center rated 5.0 out of 5 stars, ships from NV, USA, published 2025 by Independently Published.
