| 書名: | NEURAL NETWORKS & LEARNING MACHINES (3版) | |||
| 作者: | HAYKIN | |||
| 版次: | 3 | |||
| ISBN: | 9780131293762 | |||
| 出版社: | Pearson | |||
| 出版日期: | 2009/04 | |||
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#資訊
#工程 #電子與電機 #控制系統 #自動化與機器人技術 #AI人工智慧與機器學習 #生物工程 |
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書名:NEURAL NETWORKS & LEARNING MACHINES 3/E 作者:HAYKIN 出版社:PEARSON 出版日期:2009/00/00 ISBN:9780131293762
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This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. In addition to describing the mathematical principles of the topic, and its historical evolution, strong connections are drawn with underlying methods from statistical physics and current applications within science and engineering. Closely based around a well-established undergraduate course, this pedagogical text provides a solid understanding of the key aspects of modern machine learning with artificial neural networks, for students in physics, mathematics, and engineering. Numerous exercises expand and reinforce key concepts within the book and allow students to hone their programming skills. Frequent references to current research develop a detailed perspective on the state-of-the-art in machine learning research.
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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.
資訊
工程
數學與統計學
機率與統計
自然科學
健康科學
地球與環境
建築、設計與藝術
人文與社會科學
教育
語言學習與考試
法律
會計與財務
大眾傳播
觀光與休閒餐旅
考試用書
研究方法
商業與管理
經濟學
心理學
生活
生活風格商品
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