000 | 04361nam a22005775i 4500 | ||
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001 | 978-981-99-9672-8 | ||
003 | DE-He213 | ||
005 | 20240423130339.0 | ||
007 | cr nn 008mamaa | ||
008 | 240409s2024 si | s |||| 0|eng d | ||
020 |
_a9789819996728 _9978-981-99-9672-8 |
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024 | 7 |
_a10.1007/978-981-99-9672-8 _2doi |
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050 | 4 | _aQA76.76.A65 | |
072 | 7 |
_aUB _2bicssc |
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_aCOM005000 _2bisacsh |
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_aUX _2thema |
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_a005.3 _223 |
100 | 1 |
_aGamba, Jonah. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIV, 201 p. 265 illus., 164 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aTransactions on Computer Systems and Networks, _x2730-7492 |
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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 _d187615 |