Mastebroek (31 risultati)

Hardcover. Condizione: New. US SELLER SHIPS FAST FROM USA.

Softcover. Condizione: Fine. Leichte Gebrauchsspuren; Leichte Verformung / umgeknickte Seiten; Leichte Verschmutzung / Farbtonveranderung. Elfenkind vertelt het ontroerende verhaal van Gwendelyn, een jonge vrouw in het oude Keltische Wales, die op zoek is naar zingeving en bevrijding van haar sombere karakter. Haar lange reis voert haar langs religieuze plekken in haar land, maar haar zoektocht lijkt tevergeefs totdat ze iemand ontmoet die haar ogen opent. In 1955 vindt een kinderloos echtpaar in Wales een baby, Gwendelyn, met een brief van haar moeder die hen vraagt goed voor haar te zorgen. Ze adopteren haar en voeden haar op als hun eigen kind. In 1973 ontdekt schaapherder Daniel een ziek, onderkoeld meisje dat door een oude dokter wordt herkend als het kind van een manisch-depressief meisje dat hij in 1955 behandelde. Gwen, die ook de ziekte heeft, is weggelopen van haar ouders. Onderzoek onthult dat ze een defecte, inoperabele hartklep heeft, wat leidt tot haar vroege dood. Door goede zorg, haar liefde voor Daniel, het weerzien met haar adoptieouders en moeder, en haar nieuwe geloof in God, sterft ze in vrede. De auteur, die debuteerde met de novelle 'De Doler', wil met deze roman steun bieden aan mensen die manisch-depressief zijn. Hoewel het verhaal langzaam op gang komt en zwaar aanvoelt, kan het vooral voor christelijke lezers die met de ziekte te maken hebben, een bron van steun zijn.…

Condizione: Good. Oorspronkelijke omslag met 7 CD's, 8vo.

Hard Cover. Condizione: Good. No Jacket. Ex-library with the usual features. The interior is clean and tight. Binding is good. Cover shows light wear. 259 pages. Ex-Library.

Condizione: Very Good.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New.

Condizione: New. In English.

Condizione: New. In English.

Condizione: New.

Condizione: New. With an intention of returning the mathematical tools of neural networks to the biological realm of the nervous system, this work introduces in a didactic manner, two developments in neural network methodology, namely recurrence in the architecture and the use of spiking or integrate-and-fire neurons. Editor(s): Mastebroek, Henk A.K.; Vos, Johan E. Series: Mathematical Modelling: Theory and Applications. Num Pages: 271 pages, biography. BIC Classification: PSAN; UGK. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 244 x 170 x 17. Weight in Grams: 565. . 2001. Hardback. . . . .…

Taschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - The expression 'Neural Networks' refers traditionally to a class of mathematical algorithms that obtain their proper performance while they 'learn' from examples or from experience. As a consequence, they are suitable for performing straightforward and relatively simple tasks like classification, pattern recognition and prediction, as well as more sophisticated tasks like the processing of temporal sequences and the context dependent processing of complex problems. Also, a wide variety of control tasks can be executed by them, and the suggestion is relatively obvious that neural networks perform adequately in such cases because they are thought to mimic the biological nervous system which is also devoted to such tasks. As we shall see, this suggestion is false but does not do any harm as long as it is only the final performance of the algorithm which counts. Neural networks are also used in the modelling of the functioning of (sub systems in) the biological nervous system. It will be clear that in such cases it is certainly not irrelevant how similar their algorithm is to what is precisely going on in the nervous system. Standard artificial neural networks are constructed from 'units' (roughly similar to neurons) that transmit their 'activity' (similar to membrane potentials or to mean firing rates) to other units via 'weight factors' (similar to synaptic coupling efficacies).…

Buch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - The expression 'Neural Networks' refers traditionally to a class of mathematical algorithms that obtain their proper performance while they 'learn' from examples or from experience. As a consequence, they are suitable for performing straightforward and relatively simple tasks like classification, pattern recognition and prediction, as well as more sophisticated tasks like the processing of temporal sequences and the context dependent processing of complex problems. Also, a wide variety of control tasks can be executed by them, and the suggestion is relatively obvious that neural networks perform adequately in such cases because they are thought to mimic the biological nervous system which is also devoted to such tasks. As we shall see, this suggestion is false but does not do any harm as long as it is only the final performance of the algorithm which counts. Neural networks are also used in the modelling of the functioning of (sub systems in) the biological nervous system. It will be clear that in such cases it is certainly not irrelevant how similar their algorithm is to what is precisely going on in the nervous system. Standard artificial neural networks are constructed from 'units' (roughly similar to neurons) that transmit their 'activity' (similar to membrane potentials or to mean firing rates) to other units via 'weight factors' (similar to synaptic coupling efficacies).…

Condizione: New. pp. 276.

Condizione: New. With an intention of returning the mathematical tools of neural networks to the biological realm of the nervous system, this work introduces in a didactic manner, two developments in neural network methodology, namely recurrence in the architecture and the use of spiking or integrate-and-fire neurons. Editor(s): Mastebroek, Henk A.K.; Vos, Johan E. Series: Mathematical Modelling: Theory and Applications. Num Pages: 271 pages, biography. BIC Classification: PSAN; UGK. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 244 x 170 x 17. Weight in Grams: 565. . 2001. Hardback. . . . . Books ship from the US and Ireland.…

Condizione: As New. Unread book in perfect condition.

Hardcover. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

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Condizione: Hervorragend. Zustand: Hervorragend | Sprache: Niederländisch | Produktart: Bücher | Keine Beschreibung verfügbar. Geysen, François (illustratore).

Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 336 | Sprache: Niederländisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Condizione: new. Questo è un articolo print on demand.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The expression 'Neural Networks' refers traditionally to a class of mathematical algorithms that obtain their proper performance while they 'learn' from examples or from experience. As a consequence, they are suitable for performing straightforward and relatively simple tasks like classification, pattern recognition and prediction, as well as more sophisticated tasks like the processing of temporal sequences and the context dependent processing of complex problems. Also, a wide variety of control tasks can be executed by them, and the suggestion is relatively obvious that neural networks perform adequately in such cases because they are thought to mimic the biological nervous system which is also devoted to such tasks. As we shall see, this suggestion is false but does not do any harm as long as it is only the final performance of the algorithm which counts. Neural networks are also used in the modelling of the functioning of (sub systems in) the biological nervous system. It will be clear that in such cases it is certainly not irrelevant how similar their algorithm is to what is precisely going on in the nervous system. Standard artificial neural networks are constructed from 'units' (roughly similar to neurons) that transmit their 'activity' (similar to membrane potentials or to mean firing rates) to other units via 'weight factors' (similar to synaptic coupling efficacies). 276 pp. Englisch. …

Gebunden. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The expression Neural Networks refers traditionally to a class of mathematical algorithms that obtain their proper performance while they learn from examples or from experience. As a consequence, they are suitable for performing straightforward and rela.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The expression Neural Networks refers traditionally to a class of mathematical algorithms that obtain their proper performance while they learn from examples or from experience. As a consequence, they are suitable for performing straightforward and rela.…

Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The expression 'Neural Networks' refers traditionally to a class of mathematical algorithms that obtain their proper performance while they 'learn' from examples or from experience. As a consequence, they are suitable for performing straightforward and relatively simple tasks like classification, pattern recognition and prediction, as well as more sophisticated tasks like the processing of temporal sequences and the context dependent processing of complex problems. Also, a wide variety of control tasks can be executed by them, and the suggestion is relatively obvious that neural networks perform adequately in such cases because they are thought to mimic the biological nervous system which is also devoted to such tasks. As we shall see, this suggestion is false but does not do any harm as long as it is only the final performance of the algorithm which counts. Neural networks are also used in the modelling of the functioning of (sub systems in) the biological nervous system. It will be clear that in such cases it is certainly not irrelevant how similar their algorithm is to what is precisely going on in the nervous system. Standard artificial neural networks are constructed from 'units' (roughly similar to neurons) that transmit their 'activity' (similar to membrane potentials or to mean firing rates) to other units via 'weight factors' (similar to synaptic coupling efficacies). 276 pp. Englisch.…

Condizione: New. Print on Demand pp. 276 49:B&W 6.14 x 9.21 in or 234 x 156 mm (Royal 8vo) Perfect Bound on White w/Gloss Lam.

Buch. Condizione: Neu. Plausible Neural Networks for Biological Modelling | J. E. Vos (u. a.) | Buch | ix | Englisch | 2001 | Springer Netherland | EAN 9780792371922 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. …

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The expression 'Neural Networks' refers traditionally to a class of mathematical algorithms that obtain their proper performance while they 'learn' from examples or from experience. As a consequence, they are suitable for performing straightforward and relatively simple tasks like classification, pattern recognition and prediction, as well as more sophisticated tasks like the processing of temporal sequences and the context dependent processing of complex problems. Also, a wide variety of control tasks can be executed by them, and the suggestion is relatively obvious that neural networks perform adequately in such cases because they are thought to mimic the biological nervous system which is also devoted to such tasks. As we shall see, this suggestion is false but does not do any harm as long as it is only the final performance of the algorithm which counts. Neural networks are also used in the modelling of the functioning of (sub systems in) the biological nervous system. It will be clear that in such cases it is certainly not irrelevant how similar their algorithm is to what is precisely going on in the nervous system. Standard artificial neural networks are constructed from 'units' (roughly similar to neurons) that transmit their 'activity' (similar to membrane potentials or to mean firing rates) to other units via 'weight factors' (similar to synaptic coupling efficacies).Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 276 pp. Englisch.…

Buch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The expression 'Neural Networks' refers traditionally to a class of mathematical algorithms that obtain their proper performance while they 'learn' from examples or from experience. As a consequence, they are suitable for performing straightforward and relatively simple tasks like classification, pattern recognition and prediction, as well as more sophisticated tasks like the processing of temporal sequences and the context dependent processing of complex problems. Also, a wide variety of control tasks can be executed by them, and the suggestion is relatively obvious that neural networks perform adequately in such cases because they are thought to mimic the biological nervous system which is also devoted to such tasks. As we shall see, this suggestion is false but does not do any harm as long as it is only the final performance of the algorithm which counts. Neural networks are also used in the modelling of the functioning of (sub systems in) the biological nervous system. It will be clear that in such cases it is certainly not irrelevant how similar their algorithm is to what is precisely going on in the nervous system. Standard artificial neural networks are constructed from 'units' (roughly similar to neurons) that transmit their 'activity' (similar to membrane potentials or to mean firing rates) to other units via 'weight factors' (similar to synaptic coupling efficacies).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 276 pp. Englisch.…