Deep Learning - 8 Angebote vergleichen
| Preise | 2018 | 2019 | 2020 | 2021 | 2022 |
|---|---|---|---|---|---|
| Schnitt | € 70,81 | € 72,20 | € 47,46 | € 100,92 | € 61,99 |
| Nachfrage |
1
Deep Learning (2016)
~EN NWISBN: 9780262035613 bzw. 0262035618, vermutlich in Englisch, MIT Press, neu.
Lieferung aus: Schweiz, Versandfertig innert 1 - 2 Werktagen.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. 'Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. gebundene Ausgabe, 18.11.2016.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. 'Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors. gebundene Ausgabe, 18.11.2016.
2
Deep Learning
~EN NWISBN: 9780262035613 bzw. 0262035618, vermutlich in Englisch, MIT Press / The MIT Press, neu.
Lieferung aus: Deutschland, Lieferzeit 1-3 Werktage, Versandkostenfrei innerhalb von Deutschland.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.Written by three experts in the field, Deep Learning is the only comprehensive book on the subject. Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
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Deep Learning
~EN NWISBN: 9780262035613 bzw. 0262035618, vermutlich in Englisch, The MIT Press, neu.
Lieferung aus: Kanada, Lagernd, zzgl. Versandkosten.
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Books, Computers, Deep Learning, An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.-Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Books, Computers, Deep Learning, An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.-Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceXDeep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
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Deep Learning (Adaptive Computation and Machine Learning series)
~EN USISBN: 9780262035613 bzw. 0262035618, vermutlich in Englisch, The MIT Press, gebraucht.
Lieferung aus: Vereinigte Staaten von Amerika, Versandkosten nach: DEU.
Von Händler/Antiquariat, BooksRun.
The MIT Press. Very Good. Very Good. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
Von Händler/Antiquariat, BooksRun.
The MIT Press. Very Good. Very Good. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
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Deep Learning (Adaptive Computation and Machine Learning series) (2016)
~EN HC USISBN: 9780262035613 bzw. 0262035618, vermutlich in Englisch, The MIT Press, gebundenes Buch, gebraucht.
Lieferung aus: Vereinigte Staaten von Amerika, Versandkosten nach: DEU.
Von Händler/Antiquariat, BooksEntirely.
The MIT Press, 2016-11-18. Hardcover. Good.
Von Händler/Antiquariat, BooksEntirely.
The MIT Press, 2016-11-18. Hardcover. Good.
6
Deep Learning (Adaptive Computation and Machine Learning) (2017)
EN HC NWISBN: 9780262035613 bzw. 0262035618, in Englisch, 800 Seiten, The MIT Press, gebundenes Buch, neu.
Neu ab: 36,72 € (44 Angebote)
Gebraucht ab: 58,20 € (5 Angebote)
Zu den weiteren 49 Angeboten bei Amazon.de (Int.)
Lieferung aus: Deutschland, Versandkostenfrei. Tatsächliche Versandkosten können abweichen.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
7
Symbolbild
Deep Learning (2016)
~EN HC USISBN: 9780262035613 bzw. 0262035618, vermutlich in Englisch, MIT PRESS, gebundenes Buch, gebraucht.
Lieferung aus: Vereinigte Staaten von Amerika, zzgl. Versandkosten, Versandgebiet: DOM.
Von Händler/Antiquariat, Powell's Books, OR, Portland, [RE:4].
H Hardcover.
Von Händler/Antiquariat, Powell's Books, OR, Portland, [RE:4].
H Hardcover.
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