ISBN: 9780262035613

Ian Goodfellow Deep Learning. Ian Goodfellow - Deep Learning. Author: Ian Goodfellow. Ian Goodfellow is a Research Scientist at Google. Yoshua Bengio is Professor of Computer Science at t… Mehr…

Ian Goodfellow Deep Learning. Ian Goodfellow - Deep Learning. Author: Ian Goodfellow. Ian Goodfellow is a Research Scientist at Google. Yoshua Bengio is Professor of Computer Science at the Université de Montréal. Buchmeister: Deep Learning ~ Ian Goodfellow Unsere Angebote ☰ Belletristik Biografien Computer Esoterik Fachbücher Fahrzeuge Gesundheit Kinderbücher Krimis Kultur Ratgeber Reisen Sachbücher Schule Science Fiction Tiere & Natur Zeitgeschichte Ian Goodfellow Deep Learning  Ian Goodfellow - Deep Learning Art Nr.: 0262035618 ISBN 13: 9780262035613 Release Year: 2016 Published by: The MIT Press Cover: Buch Cover Format: 234x185x33 mm Pages: 775 Weight: 1330 g Language: Englisch Author: Ian Goodfellow Warengruppe: Hardcover/Naturwissenschaften, Medizin, Informatik, Technik/Informatik, EDV/Informatik NEUWARE - Portofrei innerhalb Deutschlands! Beschreibung: 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. Autoreninformationen: Ian Goodfellow is a Research Scientist at Google.<br><br>Yoshua Bengio is Professor of Computer Science at the Université de Montréal.<br><br>Aaron Courville is Assistant Professor of Computer Science at the Université de Montréal. Kurzbeschreibung Titel: Deep Learning, Einband: Buch, Autor: Ian Goodfellow, Verlag: The MIT Press, Sprache: Englisch, Seiten: 775, Maße: 234x185x33 mm, Gewicht: 1330 g, Verkäufer: rheinberg-buch-at, Schlagworte: Intelligenz / Künstliche Intelligenz KI Künstliche Intelligenz - AI Fuzzy Logik - Fuzzy Set Neuronales Netz - Neuronaler Computer - Neurocomputer COMPUTERS / Intelligence (AI) & Semantics COMPUTERS / Computer Science technology brain future stacking turing machine lasso bishop azure robotics java murphy machine learning python machine learning statistics probability coding software engineering textbook data science neural networks programming linear algebra machine learning deep learning computers computer science computer ai artificial intelligence algorithm algorithms tech superintelligence intelligence ap computer science ai books computer books psychology internet business neuroscience critical thinking Bitte beachten Sie unsere Kundeninformationen, Datenschutzerklärung und Allgemeine Geschäftsbedingungen, welche Sie in dem Feld ?Allgemeine Geschäftsbedingungen für dieses Angebot? finden. ©2010 Rheinberg Buch / Alle Rechte vorbehalten. / Alle Preise inkl. MwSt. / powered by INFORIUS , Festpreisangebot, [LT: FixedPrice], Format: 234x185x33 mm, Marke: The MIT Press, Fachbereich: Hardcover/Naturwissenschaften, Medizin, Informatik, Technik/In... Publikationstitel: Deep Learning, Produktart: Bücher, Buchtitel: Deep Learning, Film-/Fernseh-Titel: Keine Angabe, Publikationsname: Deep Learning, Musiktitel: Keine Angabe, Interpret: Keine Angabe, The MIT Press, Nov 2016<

ISBN: 9780262035613

Publisher ? : ? The MIT Press (November 18, 2016). ABOUT THE BOOK ISBN-10 ? : ? 0262035618. Product details Publisher ? … Mehr…

Publisher ? : ? The MIT Press (November 18, 2016). ABOUT THE BOOK ISBN-10 ? : ? 0262035618. Product details Publisher ? : ? The MIT Press (November 18, 2016) Language ? : ? English Hardcover ? : ? 800 pages ISBN-10 ? : ? 0262035618 ISBN-13 ? : ? 978-0262035613 Reading age ? : ? 18 years and up Grade level ? : ? 12 and up Item Weight ? : ? 2.54 pounds Dimensions ? : ? 9.1 x 7.2 x 1.1 inches ABOUT THE BOOK: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.NOTE: The item will be shipped right away using FEDEX EXPRESS with delivery in 3-5 working days (FREE SHIPPING). BOOK CONDITION-original NEW. Contact for any assistance (if you have any questions) and purchase with confidence.  , Neu, Festpreisangebot, [LT: FixedPrice], Reihe: Lies mal, Produktart: Bücher, Sprache: Englisch, Bildungsweg: Berufsschule, Buchtitel: Deep Learning, Höhe: 1.1 in, Breite: 7.2 in, Format: HARDCOVER, Personalisiert: Nein, Anzahl der Seiten: 800 pages, Fachbereich: Hardcover/Naturwissenschaften, Medizin, Informatik, Technik/In... Publikationstitel: Deep Learning, Fach: Technik, Niveau: Fortgeschrittene, Einband: Buch, Länge: 9.1 in, Gewicht: 45.5 oz, Marke: The MIT Press, Herstellungsland und -region: Vereinigte Staaten, Publikationsname: Deep Learning, The MIT Press, Nov 2016<

ISBN: 9780262035613

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… Mehr…

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.NOTE: The item will be shipped right away using FEDEX EXPRESS with delivery in 3-5 working days (FREE SHIPPING). BOOK CONDITION-original NEW. Contact for any assistance (if you have any questions) and purchase with confidence.  , Neu, Festpreisangebot, [LT: FixedPrice], Reihe: Lies mal, Produktart: Bücher, Sprache: Englisch, Bildungsweg: Berufsschule, Buchtitel: Deep Learning, Höhe: 1.1 in, Breite: 7.2 in, Format: HARDCOVER, Personalisiert: Nein, Anzahl der Seiten: 800 pages, Fachbereich: Hardcover/Naturwissenschaften, Medizin, Informatik, Technik/In... Publikationstitel: Deep Learning, Fach: Technik, Niveau: Fortgeschrittene, Einband: Buch, Länge: 9.1 in, Gewicht: 45.5 oz, Marke: The MIT Press, Herstellungsland und -region: Vereinigte Staaten, Publikationsname: Deep Learning, The MIT Press, Nov 2016<

ISBN: 9780262035613

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. "Writte… Mehr…

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. Bücher > Fremdsprachige Bücher > Englische Bücher > Sach- & Fachbücher > Naturwissenschaften & Technik 18.5 cm x 23.4 cm x 3.3 cm mm , The MIT Press, Gebundene Ausgabe, The MIT Press<

ISBN: 9780262035613

Find Deep Learning by Ian Goodfellow in Hardcover and other formats in Computers > Intelligence (AI) & Semantics. Computers 9780262035613, MIT Press