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008 181206s2019 xxu|||| o |||| 0|eng
010 _a 2019737853
020 _a9781484242391
024 7 _a10.1007/978-1-4842-4240-7
_2doi
035 _a(DE-He213)978-1-4842-4240-7
040 _aDLC
_beng
_epn
_erda
_cDLC
072 7 _aUYQ
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072 7 _aCOM004000
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072 7 _aUYQ
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082 0 4 _a006.32
_21
_bMOOJ
100 1 _aMoolayil, Jojo.
_932908
245 1 0 _aLearn keras for deep neural networks :
_ba fast-track approach to modern deep learning with python /
_cBy Jojo Moolayil.
250 _a1st ed.
260 _aNew York :
_bApress ,
_c2021.
264 1 _aBerkeley, CA :
_bApress :
_bImprint: Apress,
_c2019.
300 _axv,182p. ;
_c23 cm.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1: Deep Learning and Keras -- Chapter 2: Keras in Action -- Chapter 3: Deep Neural networks for Supervised Learning -- Chapter 4: Measuring Performance for DNN -- Chapter 5: Hyperparameter Tuning and Model Deployment -- Chapter 6: The Path Forward.
520 _aLearn, understand, and implement deep neural networks in a math- and programming-friendly approach using Keras and Python. The book focuses on an end-to-end approach to developing supervised learning algorithms in regression and classification with practical business-centric use-cases implemented in Keras. The overall book comprises three sections with two chapters in each section. The first section prepares you with all the necessary basics to get started in deep learning. Chapter 1 introduces you to the world of deep learning and its difference from machine learning, the choices of frameworks for deep learning, and the Keras ecosystem. You will cover a real-life business problem that can be solved by supervised learning algorithms with deep neural networks. You'll tackle one use case for regression and another for classification leveraging popular Kaggle datasets. Later, you will see an interesting and challenging part of deep learning: hyperparameter tuning; helping you further improve your models when building robust deep learning applications. Finally, you'll further hone your skills in deep learning and cover areas of active development and research in deep learning. At the end of Learn Keras for Deep Neural Networks, you will have a thorough understanding of deep learning principles and have practical hands-on experience in developing enterprise-grade deep learning solutions in Keras. You will: Master fast-paced practical deep learning concepts with math- and programming-friendly abstractions. Design, develop, train, validate, and deploy deep neural networks using the Keras framework Use best practices for debugging and validating deep learning models Deploy and integrate deep learning as a service into a larger software service or product Extend deep learning principles into other popular frameworks.
588 _aDescription based on publisher-supplied MARC data.
650 0 _aArtificial intelligence.
_932909
650 0 _aOpen source software.
_932910
650 0 _aComputer programming.
_932911
650 0 _aPython (Computer program language).
_932912
650 1 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
_932913
650 2 4 _aOpen Source.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I29090
_932914
650 2 4 _aPython.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I29080
_932915
776 0 8 _iPrinted edition:
_z9781484242391
776 0 8 _iPrinted edition:
_z9781484242414
776 0 8 _iPrinted edition:
_z9781484247280
906 _a0
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