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EthnomusicologyEthnomusicologists believe that all humans, not just those we call musicians, are musical, and that musicality is one of the essential touchstones of the human experience. This insight raises big questions about the nature of music and the nature of humankind, and ethnomusicologists argue that to properly address these questions, we must study music in all its geographical and historical diversity. In this Very Short Introduction, one of the foremost
Ethnomusicologists believe that all humans, not just those we call musicians, are musical, and that musicality is one of the essential touchstones of the human experience. This insight raises big questions about the nature of music and the nature of humankind, and ethnomusicologists argue that to properly address these questions, we must study music in all its geographical and historical diversity. In this Very Short Introduction, one of the foremost ethnomusicologists, Timothy Rice, offers a compact and illuminating account of this growing discipline, showing how modern researchers go about studying music from around the world, looking for insights into both music and humanity. The reader discovers that ethnomusicologists today not only examine traditional forms of music-such as Japanese gagaku, Bulgarian folk music, Javanese gamelan, or Native American drumming and singing-but also explore more contemporary musical forms, from rap and reggae to Tex-Mex, Serbian turbofolk, and even the piped-in music at the Mall of America. To investigate these diverse musical forms, Rice shows, ethnomusicologists typically live in a community, participate in and observe and record musical events, interview the musicians, their patrons, and the audience, and learn to sing, play, and dance. It's important to establish rapport with musicians and community members, and obtain the permission of those they will work with closely over the course of many months and years. We see how the researcher analyzes the data to understand how a particular musical tradition works, what is distinctive about it, and how it bears the personal, social, and cultural meanings attributed to it. Rice also discusses how researchers may apply theories from anthropology and other social sciences, to shed further light on the nature of music as a human behavior and cultural practice. About the Series:Oxford's Very Short Introductions series offers concise and original introductions to a wide range of subjects--from Islam to Sociology, Politics to Classics, Literary Theory to History, and Archaeology to the Bible. Not simply a textbook of definitions, each volume in this series provides trenchant and provocative--yet always balanced and complete--discussions of the central issues in a given discipline or field. Every Very Short Introduction gives a readable evolution of the subject in question, demonstrating how the subject has developed and how it has influenced society. Eventually, the series will encompass every major academic discipline, offering all students an accessible and abundant reference library. Whatever the area of study that one deems important or appealing, whatever the topic that fascinates the general reader, the Very Short Introductions series has a handy and affordable guide that will likely prove indispensable.
Binding Type: Paperback
Publisher: Oxford University Press, USA
Published: 01/15/2014
ISBN: 9780199794379
Pages: 151
Weight: 0.30lbs
Size: 6.80h x 4.30w x 0.60d
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4.1 ★★★★★
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Product Reviews
★★★★★ 5
Your Blueprint for Building Smarter AI!
Format: Paperback
If you're building AI and sometimes feel a bit lost, "LLM Design Patterns" by Ken Huang is like finding the secret map you've been searching for.
Ken Huang, who clearly knows his stuff (he's a renowned AI expert and works with big names like OWASP and NIST), writes in a way that just clicks, without getting bogged down in super-dense tech talk. The author even acknowledges using AI to make the language clearer for a smooth reading experience!
This book covers everything you need, from getting your data squeaky clean to making AI agents that can actually think and act autonomously. For me, the parts on Retrieval-Augmented Generation (RAG) and advanced ways to 'talk' to your AI (prompting) were particularly eye-opening and immediately useful for my projects.
Plus, it has handy code snippets that really help you grasp the ideas. While they're not ready for direct production copy-pasting, they illustrate the concepts perfectly for learning. It's not for absolute beginners – you'll want some basic Python and machine learning smarts to get the most out of it – but the effort is totally worth it. It even delves into making sure your AI is fair and unbiased, which was a real lightbulb moment for me.
This book is crammed with actionable advice; it's less about abstract theory and more about real-world solutions you can actually use. If you're serious about building impressive AI systems professionally, this is a must-read.
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Reviewed in the United States on August 7, 2025
★★★★★ 5
Great Resource when Integrating AI
Format: Kindle
This is a great resource when building systems that integrate with AI. It manages to cover the entire lifecycle and even tips for corporate environments!
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Reviewed in the United States on August 26, 2025
★★★★★ 4
Title: Really Good Book for Learning LLMs
Format: Paperback, Format: Paperback
I picked up this book after struggling with LLM implementation at work. Ken Huang explains things clearly without too much technical jargon.
The book covers everything from data preparation to building AI agents. I especially liked the chapters on RAG and prompting techniques - they helped me improve my current projects. The code examples actually work, which is nice.
Some parts are pretty advanced, so you need basic Python knowledge. I had to read a few chapters twice to fully get it. The fairness and bias detection section was eye-opening.
Good practical advice throughout. Not just theory - real solutions you can use. Worth the money if you're serious about LLM development.
Recommended for anyone building AI systems professionally.
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Reviewed in the United States on July 25, 2025
★★★★★ 5
a comprehensive guide at the intersection of generative AI and cybersecurity
Format: Kindle
This book blends deep theoretical foundations with practical frameworks and forward-looking strategies. From adversarial risk models to actionable guidance using OWASP Top 10 for LLMs and the NIST AI RMF, it offers both technical depth and operational clarity.
What makes it stand out is its balance of academic rigor and real-world CISO insights, providing a holistic perspective on securing GenAI systems. While it leans enterprise-focused, the content remains accessible to security engineers, risk managers, and policy leaders alike.
Generative AI Security is a timely and essential read for anyone working to deploy GenAI responsibly—building systems with both power and integrity in today’s fast-evolving threat landscape.
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Reviewed in the United States on July 2, 2025
★★★★★ 1
Light on substance and heavy on flaws
Format: Paperback
The book has a great list of topics, but fails to provide much substance any of them.
Most of the provided code is just comments that avoid the actual crux of the issues being discussed. (e.g. #implement the logic to validate XYZ - while the whole point of this chapter is teach how the heck we validate XYZ!)
Some parts are plain wrong, for example the part on Graph based RAG is fundamentally flawed as it assumes the text embedding and the graph embedding are in the same latent space. (This is one of many more examples).
Seems like the book was rushed, and the author has limited hands on experience (if any).
At least we know based on the amount of flaws that it was not written by an LLM
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Reviewed in the United States on December 31, 2025