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020 _a9789819996728
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024 7 _a10.1007/978-981-99-9672-8
_2doi
050 4 _aQA76.76.A65
072 7 _aUB
_2bicssc
072 7 _aCOM005000
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082 0 4 _a005.3
_223
100 1 _aGamba, Jonah.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aDeep Learning Models
_h[electronic resource] :
_bA Practical Approach for Hands-On Professionals /
_cby Jonah Gamba.
250 _a1st ed. 2024.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2024.
300 _aXIV, 201 p. 265 illus., 164 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTransactions on Computer Systems and Networks,
_x2730-7492
505 0 _aChapter 1. Basic Approaches in Object Detection and Classification by Deep Learning -- Chapter 2. Requirements for Hands-on Approach to Deep Learning -- Chapter 3. Building Deep Learning Models -- Chapter 4. The Building Blocks of Machine Learning and Deep Learning -- Chapter 5. Remote Sensing Example for Deep Learning.
520 _aThis book focuses on and prioritizes a practical approach, minimizing theoretical concepts to deliver algorithms effectively. With deep learning emerging as a vibrant field of research and development in numerous industrial applications, there is a pressing need for accessible resources that provide comprehensive examples and quick guidance. Unfortunately, many existing books on the market tend to emphasize theoretical aspects, leaving newcomers scrambling for practical guidance. This book takes a different approach by focusing on practicality while keeping theoretical concepts to a necessary minimum. The book begins by laying a foundation of basic information on deep learning, gradually delving into the subject matter to explain and illustrate the limitations of existing algorithms. A dedicated chapter is allocated to evaluating the performance of multiple algorithms on specific datasets, highlighting techniques and strategies that can address real-world challenges when deep learning is employed. By consolidating all necessary information into a single resource, readers can bypass the hassle of scouring scattered online sources, gaining a one-stop solution to dive into deep learning for object detection and classification. To facilitate understanding, the book employs a rich array of illustrations, figures, tables, and code snippets. Comprehensive code examples are provided, empowering readers to grasp concepts quickly and develop practical solutions. The book covers essential methods and tools, ensuring a complete and comprehensive coverage that enables professionals to implement deep learning algorithms swiftly and effectively. This book is designed to equip professionals with the necessary skills to thrive in the active field of deep learning, where it has the potential to revolutionize traditional problem-solving approaches. This book serves as a practical companion, enabling readers to grasp concepts swiftly and embark on building practical solutions.
650 0 _aApplication software.
650 0 _aComputer engineering.
650 0 _aComputer networks .
650 0 _aComputer vision.
650 0 _aComputer science.
650 1 4 _aComputer and Information Systems Applications.
650 2 4 _aComputer Engineering and Networks.
650 2 4 _aComputer Vision.
650 2 4 _aComputer Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789819996711
776 0 8 _iPrinted edition:
_z9789819996735
776 0 8 _iPrinted edition:
_z9789819996742
830 0 _aTransactions on Computer Systems and Networks,
_x2730-7492
856 4 0 _uhttps://doi.org/10.1007/978-981-99-9672-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c187615
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