04028cam a22005175i 4500 21661267 OSt 20220429114622.0 m |o d | cr ||||||||||| 181206s2019 xxu|||| o |||| 0|eng 2019737853 9781484242391 10.1007/978-1-4842-4240-7 doi (DE-He213)978-1-4842-4240-7 DLC eng pn rda DLC UYQ bicssc COM004000 bisacsh UYQ thema 006.32 1 MOOJ Moolayil, Jojo. 32908 Learn keras for deep neural networks : a fast-track approach to modern deep learning with python / By Jojo Moolayil. 1st ed. New York : Apress , 2021. Berkeley, CA : Apress : Imprint: Apress, 2019. xv,182p. ; 23 cm. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Chapter 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. Learn, 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. Description based on publisher-supplied MARC data. Artificial intelligence. 32909 Open source software. 32910 Computer programming. 32911 Python (Computer program language). 32912 Artificial Intelligence. https://scigraph.springernature.com/ontologies/product-market-codes/I21000 32913 Open Source. https://scigraph.springernature.com/ontologies/product-market-codes/I29090 32914 Python. https://scigraph.springernature.com/ontologies/product-market-codes/I29080 32915 Printed edition: 9781484242391 Printed edition: 9781484242414 Printed edition: 9781484247280 0 ibc origres u ncip 20 y-gencatlg ddc BK 1st 006.32 MOOJ 222731 222731 0 0 ddc 0 0 MCA AIMIT AIMIT 2022-03-24 Biblios Book Point 549.00 Bill no:6623; Bill dt:2022-03-23 0 006.32 MOOJ MCA17055 2026-05-23 00:00:00 439.20 2022-04-29 BK