Hardt Moritz (57 results)

Hardcover. Condition: Very Good. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Condition: New.

Hardcover. 15,8 x 9,8 cm. Grauer Pappband mit montiertem Rückenschild. Gelber Dreiseitenschnitt. Der Einband stellenweise leicht fleckig. Ein Ex-Libris im Innendeckel wurde entfernt. LXXII S., Frontispiz, 3 Bll., 189 Seiten / Frontispiz, 2 Bll., 191 Seiten. Innen sauber und fest. Gutes Exemplar. Neu herausgegeben und eingeleitet von Dr. Fred B. Hardt. Die beiden Titelblätter mit der Angabe Verlag Friedrich Maurer, Berlin 1785 bzw. 1786. -- Bitte Portokosten außerhalb EU erfragen! / Please ask for postage costs outside EU! / S ' il vous plait demander des frais de port en dehors de l ' UE! // Bitte beachten Sie auch unsere Fotos! / Please also note our photos! / Veuillez noter nos photos -- Wenn es draußen stürmt, können Sie es sich mit einem guten Buch gemütlich machen und ein warmes Getränk dazu genießen. Bei uns finden Sie die passende Lektüre! -- Wir kaufen Ihre werthaltigen Bücher! Lit.…

Hardback or Cased Book. Condition: New. The Emerging Science of Machine Learning Benchmarks. Book.

hardcover. Condition: As New. New with remainder mark. Buy multiples from our store to save on shipping.

hardcover. Condition: New. New with remainder mark. Buy multiples from our store to save on shipping.

Hardcover. Condition: new. Hardcover. The first comprehensive introduction to benchmarking, the engine behind progress in AIIn machine learning, researchers split their data into training and test sets, let model builders compete on the test set, and call it a benchmark. Statistical tradition prescribed locking test sets in a vault, but machine learning practitioners shared them freely. Benchmarking shouldnt have worked, but it did, and the machine learning community never figured out the science behind it. How did benchmarking, despite its flaws, lead to advances in AI? In The Emerging Science of Machine Learning Benchmarks, Moritz Hardt investigates why benchmarking works, and what purpose it serves.Hardt draws on a growing body of work that has begun to lay out the science underpinning benchmarks; what emerges is a rich landscape of theoretical and empirical observations that can inform practitioners. He begins with the foundations, both mathematical and empirical, covering enough background material to make the book self-contained. He finds that model rankings, rather than model evaluation, are the primary scientific product of machine learning benchmarks. Turning to the challenges of benchmarking large language models, Hardt explains how benchmarks influence model training, complicating direct model comparisons. As model capabilities exceed those of human evaluators, researchers are running out of ways to test new models. If benchmarks are to serve us well in the future, we must place them on solid scientific ground. With this book, Hardt lays the foundation. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New.

Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New.

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condition: As New. Unread book in perfect condition.

Condition: As New. Unread book in perfect condition.

Condition: New. 2022. Hardcover. . . . . .

Condition: 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.

Condition: New.

Hardcover. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Hardback. Condition: New. An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impactsPatterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions.Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actionsPays special attention to societal impacts and fairness in decision makingTraces the development of machine learning from its origins to todayFeatures a novel chapter on machine learning benchmarks and datasetsInvites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebraAn essential textbook for students and a guide for researchers.…

Hardcover. Condition: new. Hardcover. An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning.An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning.Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of an emerging set of proposed solutions, showing how even well-intentioned applications may give rise to objectionable results. It covers the statistical and causal measures used to evaluate the fairness of machine learning models as well as the procedural and substantive aspects of decision-making that are core to debates about fairness, including a review of legal and philosophical perspectives on discrimination. This incisive textbook prepares students of machine learning to do quantitative work on fairness while reflecting critically on its foundations and its practical utility. Introduces the technical and normative foundations of fairness in automated decision-making. Covers the formal and computational methods for characterizing and addressing problems. Provides a critical assessment of their intellectual foundations and practical utility. Features rich pedagogy and extensive instructor resources "This book offers a critical view on the current practice of machine learning, as well as proposed technical fixes for achieving fairness in automated decisionmaking"-- Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New.

HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Hardback. Condition: New.

Condition: New.

Hardback. Condition: New. An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impactsPatterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions.Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actionsPays special attention to societal impacts and fairness in decision makingTraces the development of machine learning from its origins to todayFeatures a novel chapter on machine learning benchmarks and datasetsInvites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebraAn essential textbook for students and a guide for researchers.…

Condition: New.

hardcover. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 24-48 hours from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condition: new.

Condition: New.