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Mathematics is the foundation of machine learning algorithms. To understand the shortcomings of existing algorithms and develop more effective methods, it is essential to understand the mathematical concepts underlying these algorithms and their operational principles.
The text presents various methods for applying machine learning and deep learning techniques to heterogeneous agricultural data and incorporating advanced Internet of Things-enabled technologies for web and mobile based applications for crop lifecycle tracking.
Introduces the benefits of object-oriented programming and discusses how the technology can be used to improve productivity in building software... Læs mere
Provides guidelines for performing experiments evaluating methods, techniques and tools in software engineering. It focuses on the steps that we... Læs mere
Disruptive Technologies: Transforming Industries with Smart Solutions explores the transformative impact of next-generation technologies on modern life, industry and infrastructure.
This book offers a comprehensive discussion of the growing importance of ethical considerations in algorithmic systems to introduce practical frameworks and tools for identifying ethical risks, ensuring fair outcomes, and maintaining transparency in algorithmic processes.
Introduces a model of inheritance based around five fundamental inheritance relationships. Each relationship has a conceptual basis,... Læs mere
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This book explains and outlines the use of hierarchical abstraction for project development. The author then explains how this process also simplifies the representation and analysis of information, allowing readers to extract meaning and identify relationships within a system.