hrycej tomas (22 Ergebnisse)

Zustand: Very Good. Most items will be dispatched the same or the next working day. A copy that has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Hardcover. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less 1.65.

Zustand: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Zustand: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.

Hardcover. Zustand: Good. Sound publiation with clean pages and clear content. Typical (but minimal) library markings.

Zustand: New.

25 cm, 561 g. VIII, 246 S. Gebunden. 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: Deutsch.

tapa dura. Zustand: Bien. Modular learning in neural networks : a modularized approach to neural network classification New York. 1992. 25 cm. xiii, 235 pages. Encuadernación en tapa dura de editorial con sobrecubierta. Idioma Inglés. Sixth-generation computer technology series. Includes index . ISBN: 0471571547 (=3134558=) PC188.

No jacket. Zustand: Bon. Ancien livre de bibliothèque avec équipements. Sans jaquette. Couverture différente. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Good. Former library book. No dust jacket. Different cover. Ammareal gives back up to 15% of this item's net price to charity organizations.

Hardcover. Zustand: Brand New. 226 pages. 9.25x6.10x0.71 inches. In Stock.

Kartoniert / Broschiert. Zustand: New.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success.Topics and features:Focuses on approaches supported by mathematical arguments, rather than sole computing experiencesInvestigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from themConsiders key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithmsExamines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problemAddresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrizationInvestigates the mathematical principles involves with natural language processing and computer visionKeeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire bookAlthough this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations 'beyond' the sole computing experience.

Gebunden. Zustand: New.

Taschenbuch. Zustand: Neu. Neuware -This textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success.Topics and features:Focuses on approaches supported by mathematical arguments, rather than sole computing experiencesInvestigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from themConsiders key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithmsExamines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problemAddresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrizationInvestigates the mathematical principles involves with natural language processing and computer visionKeeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire bookAlthough this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations ¿beyond¿ the sole computing experience.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 380 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success.Topics and features:Focuses on approaches supported by mathematical arguments, rather than sole computing experiencesInvestigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from themConsiders key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithmsExamines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problemAddresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrizationInvestigates the mathematical principles involves with natural language processing and computer visionKeeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire bookAlthough this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations 'beyond' the sole computing experience.

Buch. Zustand: Neu. Neuware -This textbook aims to point out the most important principles of data analysis from the mathematical point of view. Specifically, it selected these questions for exploring: Which are the principles necessary to understand the implications of an application, and which are necessary to understand the conditions for the success of methods used Theory is presented only to the degree necessary to apply it properly, striving for the balance between excessive complexity and oversimplification. Its primary focus is on principles crucial for application success.Topics and features:Focuses on approaches supported by mathematical arguments, rather than sole computing experiencesInvestigates conditions under which numerical algorithms used in data science operate, and what performance can be expected from themConsiders key data science problems: problem formulation including optimality measure; learning and generalization in relationships to training set size and number of free parameters; and convergence of numerical algorithmsExamines original mathematical disciplines (statistics, numerical mathematics, system theory) as they are specifically relevant to a given problemAddresses the trade-off between model size and volume of data available for its identification and its consequences for model parametrizationInvestigates the mathematical principles involves with natural language processing and computer visionKeeps subject coverage intentionally compact, focusing on key issues of each topic to encourage full comprehension of the entire bookAlthough this core textbook aims directly at students of computer science and/or data science, it will be of real appeal, too, to researchers in the field who want to gain a proper understanding of the mathematical foundations ¿beyond¿ the sole computing experience.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 228 pp. Englisch.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 235 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Zustand: New.

Gebunden. Zustand: New.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Das Buch präsentiert eine Methodik für robuste Regelung, wie sie für sicherheitskritische Anwendungen wie autonomes Fahren erforderlich ist. Sie deckt alle notwendigen Schritte ab: quantitative Anforderungen an die Robustheit, Modellidentifikation aus Messdaten, Reglerentwurf und Maßnahmen bei auftretenden Instabilitäten. Alle Schritte sind praktisch durchführbar, und tragen dem typischen Qualifikationsprofil eines Entwicklungsingenieurs Rechnung, ohne enzyklopädisch auf die erhebliche Breite und Tiefe der Theorie der robusten Regelung zurückgreifen zu müssen. Um die dargestellten Algorithmen detailliert nachvollziehbar zu machen, kann die verwendete Software von der Verlags-Webseite als Zusatzmaterial heruntergeladen werden.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Deutsch | Produktart: Bücher | Keine Beschreibung verfügbar.