Data Communications and Computer Networks: A Business User`s Approach (9版)

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●“REMEMBER THIS…” INSTALLMENTS DIRECT STUDENTS TO CONSIDER CONCEPTS WITHIN A LARGER CONTEXT. These interesting learning features encourage readers to thinking about how the principles they've just studied fit into the "big picture." Students do this for each section before progressing to the next section. ●"OUTSIDE THE BOX" EXERCISES EXTEND LEARNING BEYOND THE CHAPTER'S PRINTED CONCEPTS. These special exercises, embedded throughout each chapter, challenge students to explore ideas beyond those explicitly outlined in the chapter. This broadens students' professional skills, while sharpening their critical thinking. ●“SELF-CHECK” QUESTIONS HELP STUDENTS DEVELOP METACOGNITION SKILLS. This proven, self-guided review ensures students understand the material while encouraging higher-order thinking skills. Solutions for this self-guided review appear in an appendix. ●UPDATED DISCUSSION PROMPTS AND GRADING RUBRICS ENCOURAGE EFFECTIVE LIFELONG LEARNING HABITS. Thought-provoking discussion prompts throughout this edition, as well as easy-to-follow grading rubrics, encourage students to reflect on their learning progress. These proven features help you foster and support the development of positive lifelong learning habits. 目錄 1. Introduction to Data Communications and Computer Networks. 2. Conducted and Radiated Media. 3. Fundamentals of Data and Signals. 4. Frames and Errors. 5. Wired and Wireless Local Area Networks. 6. Network Systems and Software. 7. The Internet. 8. Risk, Security, and Compliance. 9. Wide Area Networks. 10. Connecting Networks and Resources. 11. Network Design and Management. 12. Business Principles in IT.

SYSTEMS ENGINEERING NEURAL NETWORKS

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DESCRIPTION SYSTEMS ENGINEERING NEURAL NETWORKS A complete and authoritative discussion of systems engineering and neural networks In Systems Engineering Neural Networks, a team of distinguished researchers deliver a thorough exploration of the fundamental concepts underpinning the creation and improvement of neural networks with a systems engineering mindset. In the book, you’ll find a general theoretical discussion of both systems engineering and neural networks accompanied by coverage of relevant and specific topics, from deep learning fundamentals to sport business applications. Readers will discover in-depth examples derived from many years of engineering experience, a comprehensive glossary with links to further reading, and supplementary online content. The authors have also included a variety of applications programmed in both Python 3 and Microsoft Excel. The book provides: A thorough introduction to neural networks, introduced as key element of complex systems Practical discussions of systems engineering and forecasting, complexity theory and optimization and how these techniques can be used to support applications outside of the traditional AI domains Comprehensive explorations of input and output, hidden layers, and bias in neural networks, as well as activation functions, cost functions, and back-propagation Guidelines for software development incorporating neural networks with a systems engineering methodology Perfect for students and professionals eager to incorporate machine learning techniques into their products and processes, Systems Engineering Neural Networks will also earn a place in the libraries of managers and researchers working in areas involving neural networks.