Deep Learning Models A Practical Approach for Hands-On Professionals /
Gamba, Jonah.
Deep Learning Models A Practical Approach for Hands-On Professionals / [electronic resource] : by Jonah Gamba. - 1st ed. 2024. - XIV, 201 p. 265 illus., 164 illus. in color. online resource. - Transactions on Computer Systems and Networks, 2730-7492 . - Transactions on Computer Systems and Networks, .
Chapter 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.
This 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.
9789819996728
10.1007/978-981-99-9672-8 doi
Application software.
Computer engineering.
Computer networks .
Computer vision.
Computer science.
Computer and Information Systems Applications.
Computer Engineering and Networks.
Computer Vision.
Computer Science.
QA76.76.A65
005.3
Deep Learning Models A Practical Approach for Hands-On Professionals / [electronic resource] : by Jonah Gamba. - 1st ed. 2024. - XIV, 201 p. 265 illus., 164 illus. in color. online resource. - Transactions on Computer Systems and Networks, 2730-7492 . - Transactions on Computer Systems and Networks, .
Chapter 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.
This 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.
9789819996728
10.1007/978-981-99-9672-8 doi
Application software.
Computer engineering.
Computer networks .
Computer vision.
Computer science.
Computer and Information Systems Applications.
Computer Engineering and Networks.
Computer Vision.
Computer Science.
QA76.76.A65
005.3