Alexander volz (26 risultati)

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Perfect Paperback. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 9-10 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Paperback or Softback. Condizione: New. Machine Learning. A Guideline for its Usability in Production Systems. Book.

Condizione: New.

48 pp.; 29.6 x 21 cm.; glue bound; black-and-white & color; edition size unknown; unsigned and unnumbered; offset-printed Exhibition catalogue published in conjunction with show held April/May 1988. Artists include John Alexander, Lorenzo Bonechi, Peter Booth, Steven Campbell, Sandro Chia, Giuseppe Gallo, Jim Gingerich, Judy Glantzman, Red Grooms, Jin Sub Han, Hans D'Hollosy, Peter Howson, Maria De La Paz Jaramillo, Earl Linderman, Jorg Madlener, Wade Miller, Hugh O'Donnell, Mimmo Paladino, Jim Peters, Miguel Rasero, Huck Snyder,Many Volz, Adrian Wiszniewski, and Joan Worth. Contents include Collector's Commentary by Susan Kasen and Robert D. Summer, a text by Donald Kuspit, and artists' biographies. Cover features image by Jim Gingerich, "Honey There Is More," 1986. Very Good. Light rubbing of covers. Contents clean and unmarked. Includes folded photocopy of "Instant collection, timeless appeal," a review of the exhibition by Steven Litt published by The News and Observer, Raleigh, NC, April 17, 1988.…

Taschenbuch. Condizione: Sehr gut. Gebraucht - Sehr gut sg - ungelesenes mängelexemplar, gestempelt, mit leichten lagerspuren.

Condizione: New.

Paperback. Condizione: New. 1.

Condizione: New.

Condizione: New.

Condizione: As New. Unread book in perfect condition.

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Bachelor Thesis from the year 2017 in the subject Engineering - Mechanical Engineering, grade: 1,3, RWTH Aachen University, language: English, abstract: This thesis provides an especially designed overview for the needs of decision makers in the production industry on the field of machine learning. By concerning economic and technological factors, as well as the individual challenges for companies, the goal of this thesis is to serve as a guideline for the usage of machine learning in production systems.After the revolutionary change caused by the introduction of the steam engine, the production line, electronics and IT, into the manufacturing industry, a new disrupting change is expected. Nowadays the rapidly increasing digitalization of the economy leads to the fourth industrial revolution. This global phenomenon is called 'Industrie 4.0' (GER) or 'Smart Factory' (US), and it combines production technology with information and communication technology. Especially, data based optimization in production is one of the predominant goals of Industrie 4.0. For the automatized analysis of large amounts of data, machine learning is an effective instrument and therefore a central element in Industrie 4.0.Recent progress in machine learning has been driven by the development of new learning algorithms and by the increasing availability of data and low-cost computation power. For many applications - from computer vision to adaptive robots - it was very difficult to devise deterministic rules. However, for these applications, it is possible to collect data, and now the idea is to use algorithms that learn from data, instead of being manually programmed. Thus, machine learning has the potential to transform data into valuable knowledge for decision making, while making improvements possible to the production system, with approaches such as predictive maintenance. The transfer of machine learning from the lab to the 'real world' leads to an increased interest in learning techniques, demanding further effort in explaining, on how machine learning works, and what it can be used for in other disciplines.However, the entry barrier to the diverse field of machine learning is high. With many different algorithms, theories and methods, it is hard to oversee, and therefore its influence remains limited. In addition, a recent study states that about 47% of jobs in the US are at high risk of computerization within the next decades. Therefore, employees feel insecure, and demand answers on what effect machine learning will have on their future role in the factory.…

Paperback. Condizione: New. 1.

Hardcover Pappe (rot). Zustand: Exemplare einer Privatbibliothek mit Kennungen (Rückenschild, Innenstempel). Rücken, Ecken, Kanten berieben. Sprache: Deutsch Gewicht in Gramm: 3000.

Hardcover Leinen (rot). 18 Bände aus den Jahrgängen 1908-1918. Zustand: keine Eintragungen. Rücken, Ecken, Kanten berieben leicht berieben, mancher Band am Einband mit Flecken, die Gelenke sind locker. Sprache: Deutsch Gewicht in Gramm: 4500.

Hardcover. Condizione: Sehr gut. 2 Kisten, sehr gut erhalten, lagerten in verschlossen in den Kisten.

Condizione: Very Good. Auflage: 2013.

Hardcover Leinen (rot). 8600 Seiten 43 Bände aus den Jahrgängen 1899-1927. Zustand: keine Beschädigung, keine Eintragungen. Exemplar einer Privatbibliothek mit Kennungen. Rücken, Ecken, Kanten berieben. Beim Band 12, 1926 hat der vordere Buchdeckel und die ersten zwei Blätter einen vergangenen Feuchtigkeitsschaden. Band 7, 1917 hat einen Knick im Buchdeckel. Die anderen Bände haben auch Gebrauchsspuren und durchweg lockere Gelenke. Sprache: Deutsch Gewicht in Gramm: 10000.…

Condizione: gut. Rustico Vecchio - Das Erbe Andrins In deutscher Sprache. pages.

Hardcover. Condizione: gut. 2009. John Young - Bonhoeffer in Harlem In deutscher Sprache. pages.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Bachelor Thesis from the year 2017 in the subject Engineering - Mechanical Engineering, grade: 1,3, RWTH Aachen University, language: English, abstract: This thesis provides an especially designed overview for the needs of decision makers in the production industry on the field of machine learning. By concerning economic and technological factors, as well as the individual challenges for companies, the goal of this thesis is to serve as a guideline for the usage of machine learning in production systems.After the revolutionary change caused by the introduction of the steam engine, the production line, electronics and IT, into the manufacturing industry, a new disrupting change is expected. Nowadays the rapidly increasing digitalization of the economy leads to the fourth industrial revolution. This global phenomenon is called 'Industrie 4.0' (GER) or 'Smart Factory' (US), and it combines production technology with information and communication technology. Especially, data based optimization in production is one of the predominant goals of Industrie 4.0. For the automatized analysis of large amounts of data, machine learning is an effective instrument and therefore a central element in Industrie 4.0.Recent progress in machine learning has been driven by the development of new learning algorithms and by the increasing availability of data and low-cost computation power. For many applications - from computer vision to adaptive robots - it was very difficult to devise deterministic rules. However, for these applications, it is possible to collect data, and now the idea is to use algorithms that learn from data, instead of being manually programmed. Thus, machine learning has the potential to transform data into valuable knowledge for decision making, while making improvements possible to the production system, with approaches such as predictive maintenance. The transfer of machine learning from the lab to the 'real world' leads to an increased interest in learning techniques, demanding further effort in explaining, on how machine learning works, and what it can be used for in other disciplines.However, the entry barrier to the diverse field of machine learning is high. With many different algorithms, theories and methods, it is hard to oversee, and therefore its influence remains limited. In addition, a recent study states that about 47% of jobs in the US are at high risk of computerization within the next decades. Therefore, employees feel insecure, and demand answers on what effect machine learning will have on their future role in the factory. 64 pp. Englisch.…

LeatherBound. Condizione: New. BOOKS ARE EXEMPT FROM IMPORT DUTIES AND TARIFFS; NO EXTRA CHARGES APPLY. LeatherBound edition. Condition: New. Reprinted from 1882 edition. Leather Binding on Spine and Corners with Golden leaf printing on spine. NO changes have been made to the original text. This is NOT a retyped or an ocr'd reprint. Illustrations, Index, if any, are included in black and white. Each page is checked manually before printing. Pages: 762 As this print on demand book is reprinted from a very old book, there could be some missing or flawed pages, but we always try to make the book as complete as possible. Fold-outs, if any, are not part of the book. If the original book was published in multiple volumes then this reprint is of only one volume, not the whole set. Sewing binding for longer life, where the book block is actually sewn (smythe sewn/section sewn) with thread before binding which results in a more durable type of binding. Pages: 762 Language: German.…

LeatherBound. Condizione: New. BOOKS ARE EXEMPT FROM IMPORT DUTIES AND TARIFFS; NO EXTRA CHARGES APPLY. LeatherBound edition. Condition: New. Reprinted from 1883 edition. Leather Binding on Spine and Corners with Golden leaf printing on spine. NO changes have been made to the original text. This is NOT a retyped or an ocr'd reprint. Illustrations, Index, if any, are included in black and white. Each page is checked manually before printing. Pages: 810 As this print on demand book is reprinted from a very old book, there could be some missing or flawed pages, but we always try to make the book as complete as possible. Fold-outs, if any, are not part of the book. If the original book was published in multiple volumes then this reprint is of only one volume, not the whole set. Sewing binding for longer life, where the book block is actually sewn (smythe sewn/section sewn) with thread before binding which results in a more durable type of binding. Pages: 810 Language: German.…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Bachelor Thesis from the year 2017 in the subject Engineering - Mechanical Engineering, grade: 1,3, RWTH Aachen University, language: English, abstract: This thesis provides an especially designed overview for the needs of decision makers in the production industry on the field of machine learning. By concerning economic and technological factors, as well as the individual challenges for companies, the goal of this thesis is to serve as a guideline for the usage of machine learning in production systems.After the revolutionary change caused by the introduction of the steam engine, the production line, electronics and IT, into the manufacturing industry, a new disrupting change is expected. Nowadays the rapidly increasing digitalization of the economy leads to the fourth industrial revolution. This global phenomenon is called ¿Industrie 4.0¿ (GER) or ¿Smart Factory¿ (US), and it combines production technology with information and communication technology. Especially, data based optimization in production is one of the predominant goals of Industrie 4.0. For the automatized analysis of large amounts of data, machine learning is an effective instrument and therefore a central element in Industrie 4.0.Recent progress in machine learning has been driven by the development of new learning algorithms and by the increasing availability of data and low-cost computation power. For many applications - from computer vision to adaptive robots ¿ it was very difficult to devise deterministic rules. However, for these applications, it is possible to collect data, and now the idea is to use algorithms that learn from data, instead of being manually programmed. Thus, machine learning has the potential to transform data into valuable knowledge for decision making, while making improvements possible to the production system, with approaches such as predictive maintenance. The transfer of machine learning from the lab to the ¿real world¿ leads to an increased interest in learning techniques, demanding further effort in explaining, on how machine learning works, and what it can be used for in other disciplines.However, the entry barrier to the diverse field of machine learning is high. With many different algorithms, theories and methods, it is hard to oversee, and therefore its influence remains limited. In addition, a recent study states that about 47% of jobs in the US are at high risk of computerization within the next decades. Therefore, employees feel insecure, and demand answers on what effect machine learning will have on their future role in the factory.Books on Demand GmbH, Überseering 33, 22297 Hamburg 64 pp. Englisch.…

Taschenbuch. Condizione: Neu. Machine Learning. A Guideline for its Usability in Production Systems | Alexander Volz | Taschenbuch | 64 S. | Englisch | 2019 | GRIN Verlag | EAN 9783668968448 | Verantwortliche Person für die EU: GRIN Publishing GmbH, Waltherstr. 23, 80337 München, info[at]grin[dot]com | Anbieter: preigu Print on Demand. …