04090nam a22004935i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001600153072001500169072002300184072001400207100007500221245011800296250001800414264007200432300006700504336002600571337002600597338003600623347002400659490006200683505032600745520199601071650002603067650002603093650002503119650002103144650002203165650005103187650003903238650002103277650002203298710003403320773002603354776003603380776003603416776003603452830006203488856004603550978-981-99-9672-8DE-He21320251030111812.0cr nn 008mamaa240409s2024    si |    s    |||| 0|eng d  a97898199967287 a10.1007/978-981-99-9672-82doi 4aQA76.76.A65 7aUB2bicssc 7aCOM0050002bisacsh 7aUX2thema1 aGamba, Jonah.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aDeep Learning Modelsh[electronic resource] :bA Practical Approach for Hands-On Professionals /cby Jonah Gamba.  a1st ed. 2024. 1aSingapore :bSpringer Nature Singapore :bImprint: Springer,c2024.  aXIV, 201 p. 265 illus., 164 illus. in color.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  aonline resourcebcr2rdacarrier  atext filebPDF2rda1 aTransactions on Computer Systems and Networks,x2730-74920 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.  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. 0aApplication software. 0aComputer engineering. 0aComputer networks . 0aComputer vision. 0aComputer science.14aComputer and Information Systems Applications.24aComputer Engineering and Networks.24aComputer Vision.24aComputer Science.2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978981999671108iPrinted edition:z978981999673508iPrinted edition:z9789819996742 0aTransactions on Computer Systems and Networks,x2730-749240uhttps://doi.org/10.1007/978-981-99-9672-8