Editore: Packt Publishing (edition ), 2020
ISBN 10: 1838647295 ISBN 13: 9781838647292
Lingua: Inglese
Da: BooksRun, Philadelphia, PA, U.S.A.
EUR 25,18
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Aggiungi al carrelloPaperback. Condizione: Good. Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
Da: Goodwill Industries of VSB, Oxnard, CA, U.S.A.
EUR 25,96
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Aggiungi al carrellopaperback. Condizione: Good. The book is nice and 100% readable, but the book has visible wear which may include stains, scuffs, scratches, folded edges, sticker glue, torn on front page,highlighting, notes, and worn corners.
Da: thebookforest.com, San Rafael, CA, U.S.A.
EUR 27,20
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Aggiungi al carrellopaperback. Condizione: New. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010.
EUR 40,78
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Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 39,57
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Editore: Packt Publishing 6/12/2020, 2020
ISBN 10: 1838647295 ISBN 13: 9781838647292
Lingua: Inglese
Da: BargainBookStores, Grand Rapids, MI, U.S.A.
EUR 43,85
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Aggiungi al carrelloPaperback or Softback. Condizione: New. Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks 1.38. Book.
Da: California Books, Miami, FL, U.S.A.
EUR 44,50
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EUR 45,45
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Aggiungi al carrelloCondizione: As New. Unread book in perfect condition.
Da: Russell Books, Victoria, BC, Canada
EUR 47,94
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Aggiungi al carrelloPaperback. Condizione: New. Special order direct from the distributor.
Da: Chiron Media, Wallingford, Regno Unito
EUR 38,84
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 43,98
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Aggiungi al carrelloCondizione: New. In.
EUR 41,86
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EUR 47,80
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Da: Celler Versandantiquariat, Eicklingen, Germania
Membro dell'associazione: GIAQ
EUR 40,00
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Aggiungi al carrelloPackt Publishing, Birmingham, 2020, 341 Seiten, kartoniert, Quart---- Text in Englisch - 700 Gramm.
EUR 48,76
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Aggiungi al carrelloCondizione: New. The main aim of this book is to make the advanced mathematical background accessible to someone with a programming background. This book will equip the readers with not only deep learning architectures but the mathematics behind them. With this book, you w.
Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 76,41
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Aggiungi al carrelloPaperback. Condizione: New. New. book.
Da: Majestic Books, Hounslow, Regno Unito
EUR 53,22
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 345.
Da: THE SAINT BOOKSTORE, Southport, Regno Unito
EUR 48,52
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Aggiungi al carrelloPaperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 526.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 62,74
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architecturesKey FeaturesUnderstand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networksLearn the mathematical concepts needed to understand how deep learning models functionUse deep learning for solving problems related to vision, image, text, and sequence applicationsBook DescriptionMost programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you'll explore CNN, recurrent neural network (RNN), and GAN models and their application.By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.What you will learnUnderstand the key mathematical concepts for building neural network modelsDiscover core multivariable calculus conceptsImprove the performance of deep learning models using optimization techniquesCover optimization algorithms, from basic stochastic gradient descent (SGD) to the advanced Adam optimizerUnderstand computational graphs and their importance in DLExplore the backpropagation algorithm to reduce output errorCover DL algorithms such as convolutional neural networks (CNNs), sequence models, and generative adversarial networks (GANs)Who this book is forThis book is for data scientists, machine learning developers, aspiring deep learning developers, or anyone who wants to understand the foundation of deep learning by learning the math behind it. Working knowledge of the Python programming language and machine learning basics is required.