Guangyu (124 Ergebnisse)

Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc. Zhao, Guangyu (illustrator).

paperback. Zustand: Very Good.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: Fine. French édition - Livre issu de déstockage qui présente des petites marques sur la couverture et/ou les pourtours mais reste en très bon état d'ensemble. Expédition soignée avec suivi postal dans une enveloppe à bulles depuis la France. 24x19x1cm. 2016. Broché. 128 pages. Fine.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

XXI, 537 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Zustand: VERY GOOD. 2nd printing. A story told in the mountains of Tibet. An old couple are tricked by a rogue into giving him their beautiful daughter in marriage but she is rescued by a handsome young hunter, who in turn tricks the rogue. Illustrated in full color throughout by Yoa Yu-tuo and Yoa Yu-hsin. 92 pp. Very good in illustrated wrappers (some toning).…

Paperback. Zustand: Brand New. 272 pages. 3.94x3.94x2.36 inches. In Stock.

Eine rechtshistorische Untersuchung. XV, 161 S. OPp, guter Zustand. Rechtshistorische Reihe 408.

Zustand: As New. Guangyu, Li (illustrator).

Zustand: As New. Guangyu, Li (illustrator).

Zustand: New. In English.

Zustand: New.

Taschenbuch. Zustand: Neu. Neuware - What geometry does a learning system discover in data-and what does it impose through architecture, objectives, and representation How should we compare representations across models or modalities, and when do claims about manifolds, symmetry, topology, or emergent structure have mathematical content rather than visual appeal Geometric Foundations of AI (II) develops the object-side geometry of modern machine learning. It begins with group actions, representation theory, and equivariant architectures, then builds toward information bottlenecks, contrastive and self-supervised representation geometry, non-Euclidean and hyperbolic embeddings, manifold hypotheses and intrinsic dimension, representation diagnostics, alignment and transfer, topological data analysis, and the geometry of foundation-model representations. A recurring theme is that a geometric claim becomes meaningful only after the object, metric, symmetry, scale, and estimator have been specified. Written for advanced undergraduates, graduate students, and researchers in machine learning, statistics, geometry, and applied mathematics, the book combines exact mathematics with carefully delimited model-dependent and empirical claims. It is both a technical foundation for geometric and representation learning and a guide to deciding which geometric interpretations are justified by the evidence.…

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - Continuous-time mathematics has become a common language across modern artificial intelligence. Yet the same words-gradient flow, diffusion, entropy, sampling, control, and equilibrium-often refer to different mathematical objects, metrics, and regimes. This book is built around making those distinctions precise. Stochastic Dynamics for AI develops deterministic and stochastic dynamics from first principles and then uses them to connect Fokker-Planck equations and Wasserstein gradient flows; diffusion and flow-based generative models; Langevin, Hamiltonian, and non-reversible sampling; particle and variational inference; stochastic-gradient dynamics and the edge of stability; entropy-regularized control and continuous-time reinforcement learning; mean-field limits and games; and path-space relative entropy and Schrödinger bridges. Rather than presenting these subjects as isolated toolkits, the book asks a harder question: when does an idea transfer from one domain to another Each major connection is separated into what is exact, what requires a limiting argument or a change of representation, and what fails outside its stated assumptions. Scope boxes, counterexamples, comparison tables, and four tiers of exercises make the hypotheses-and the boundaries of the conclusions-part of the mathematics. Designed for advanced undergraduates, graduate students, and researchers in mathematics, statistics, machine learning, and related fields, this volume offers a rigorous guide to the continuous-time structures that underlie learning, generation, sampling, optimization, control, and interacting AI systems.…

Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Zustand: New.

Paperback. Zustand: Brand New. 210 pages. French language. 8.50x0.57x8.50 inches. In Stock.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - What changes when a learning system does more than predict-when it acts, changes what it will observe next, remembers a history, and must control a process over time Sequential Decision Making for AI develops the mathematical language for that setting. Beginning with decision theory, Markov decision processes, bandits, and dynamic programming, it builds toward reinforcement learning under exploration, function approximation, offline data, partial observability, planning, constraints, and multi-agent interaction. The unifying theme is interaction: once actions influence future data, the objects that govern learning change. The book separates familiar ideas from the assumptions that make them valid. Representability is distinguished from learnability; Bellman closure from mere function-class membership; offline sample size from policy coverage; belief-state sufficiency from minimal memory; and statistical information from computational access. Upper bounds, lower bounds, and algorithmic guarantees are tied to the observation model, interaction protocol, horizon, and resource being counted. The same framework is connected to modern foundation-model post-training and agent design: behavioural cloning, preference and reward modelling, KL-regularised optimisation, offline evaluation, context and recurrent memory, test-time planning, tool use, constrained control, and multi-agent interaction. These are interpreted through theorem-native quantities such as interaction rounds, independent samples, effective horizon, coverage, planning depth, memory bits, and computation. Written for graduate students and researchers in machine learning, statistics, applied mathematics, control, and AI, this volume is not an algorithm catalogue. It is a resource-aware guide to what sequential-learning theorems establish, what they leave open, and what additional assumptions are required before they become claims about real AI agents.…