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Joseph Walker
Joseph Walker

Book Review: Programming Collective Intelligence


But there perhaps we broach an issue and a word of warning. In many cases the explanation of the algorithm is tied closely to the Python code (available from the author's blog [1]), so lack of familiarity with Python might present a problem. That said, if the reader is familiar with other programming languages and has a good grasp of programming principles then the code samples are understandable with a little work. There are even efforts on the Web to port the code samples to other languages [2] and I would recommend this approach to anyone who really wants to understand the algorithms presented. I have long been dismissive of Python, but through this book even my hard heart began to soften at the facility and usefulness of the language!




Book Review: Programming Collective Intelligence



The penultimate two chapters of the book change tack, with Chapter 10 examining ways of discovering features in a dataset (which then form the basis of classification) and Chapter 11 taking you into the fascinating (and astonishing) world of genetic programming. The final chapter is very useful as it summarises the algorithms discussed in the rest of the book and is designed as the starting point for any new problem the reader might encounter to which these 'Collective Intelligence' techniques can be applied.


I bought Programming Collective Intelligence years ago. My idea at that time was to use it as a basis for practical applications as I learned Python. I picked a topic that looked interesting, say genetic programming, and experimented with the example code to test my Python fluency. In the process I picked up the basics of the most common families of machine learning algorithms. Since then I've returned over and over again to Programming Collective Intelligence. But now I use the book as a refresher or introduction to different machine learning domains.


In Practical Artificial Intelligence Programming with Python: From Zero to Hero, veteran educator and photophysicist Dr. Perry Xiao delivers a thorough introduction to one of the most exciting areas of computer science in modern history. The book demystifies artificial intelligence and teaches readers its fundamentals from scratch in simp...


Learn how to be more productive with Scala, a new multi-paradigm language for the Java Virtual Machine (JVM) that integrates features of both object-oriented and functional programming. With this book, you'll discover why Scala is ideal for highly scalable, component-based applications that support concurrency and distribution.Progra...


Collective Intelligence in Action is a hands-on guidebook for implementing collective-intelligence concepts using Java. It is the first Java-based book to emphasize the underlying algorithms and technical implementation of vital data gathering and mining techniques like analyzing trends, discovering relationships, and making predictions. It provides a pragmatic approach to personalization by combining content-based analysis with collaborative approaches.


Following a running example in which you harvest and use information from blogs, you learn to develop software that you can embed in your own applications. The code examples are immediately reusable and give the Java developer a working collective intelligence toolkit.


This is the place to find book reviews on all aspects of artificial intelligence and machine learning, from classic 'strong' AI to genetic algorithms to applications such as data mining. Books range from populist expositions to academic and university texts.


What do ant colonies, slime moulds and the teeming streets of Victorian Manchester have in common? They are all, according to Steven Johnson, examples of emergent systems. Emergent phenomena, such as the division of a city into discrete neighbourhoods or the apparent collective intelligence of an ant colony, are by products of the interactions of thousands of simpler 'agents'. An individual ant is fairly unintelligent and is largely driven by instinct. An ant follows a few simple rules: get food, dump waste, tend young, defend the colony. It has limited means of communications with its fellow ants, and what there is is largely based on pheromone signalling.


This advanced machine learning book provides a thorough introduction to machine learning in just (a little over) one hundred pages. It depicts information about artificial intelligence systems that are so simple to understand that you will be ready to discuss fundamental ideas in an interview.


This advanced machine learning book is perfect for people who have expertise in coding and programming but need to gain more knowledge of the mathematical and statistical aspects of machine learning. The book uses case studies that present real-world applications of machine learning algorithms, which aid in grounding mathematical theories.


Everybody should be able to read this book. It is unnecessary to have any prior understanding of calculus, linear algebra, programming, statistics, probability, or any of these topics to benefit from this series.


In Threadless, anyone who wants to can design a T-shirt, submit that design to a weekly contest and then rate their favorite designs. From the entries receiving the highest ratings, the company selects winning designs, puts them into production and gives prizes and royalties to the winning designers. In this way, the company harnesses the collective intelligence of a community of over 500,000 people to design and select T-shirts.


Learn programming, web development & DevOps with tutorials, courses & books from TopTechSkills.com. If you have any questions, feedback or would like to find out more, please reach out on social media: I am active on YouTube, Twitter and Facebook.


Using Java, this book presents basic algorithms that both analysts and programmers will need to know. Perhaps most importantly, Algorithms is a no-fuss manual that just covers algorithms. There is a companion website for the book hosted by Princeton that also includes code tests. This is the perfect manual for those who are digging into programming and those who are preparing for interview questions.


Programming is the type of discipline that requires countless hours of dedication to reading and writing code, rather than purely theoretical learning from books. Yet programming books can be enlightening and provide guidance while one attempts to hone their craft in the pursuit of mastery.


As marketing professionals communicate value and manage customer relationships, they must target changing markets, and personalize offers to individual customers. With the recent adoption of large-scale, Internet-based information systems, marketing professionals now face large volumes of complex data, including detailed purchase and service transactions, social network links, click streams, blogs, comments and inquiries. While traditional marketing methodologies struggled to produce actionable insights from such information quickly, emerging collective intelligence techniques enable marketing professionals to understand and act on the observed behaviors, preferences and ideas of groups of people. Marketing professionals apply collective intelligence technology to create behavioral models and apply them for targeting and personalization. As they analyze preferences, match products to customers, discover groups of similar consumers, and construct pricing models, they generate significant competitive advantage. In this chapter, we highlight publications of interest, describe analytic processes, review techniques, and present a case study of matching products to customers.


In case you are a programmer now interested in data crunching, then this book is perfect for you! (Lets first clarify that the Hacker in the title refers to a good programmer and not a secretive computer cracker!) So this book will help you get started with Machine Learning using lots of hands-on case studies rather than boring math-heavy presentations that are more common.Machine Learning for Hackers focuses on specific problems in each chapter such as classification, prediction, optimization, and recommendation. It will also teach you to analyze different sample datasets and write simple machine learning algorithms in the R programming language. Buy Machine Learning for Hackers Book


This book by Andriy Burkov explains the various topics of machine learning topics in a mere 100 pages. Written in an easy-to-follow manner, the machine learning book is endorsed by reputed thought leaders to the likes of the Director of Research at Google, Peter Norvig, and Sujeet Varakhedi, Head of Engineering at eBay. Post a thorough reading of the book, the reader will be able to build and appreciate complex Artificial intelligence systems, clear and ML-based interviews, and even start their own ml-based business.


Awad, E., Bonnefon, J., Caminada, M., Malone, T., & Rahwan, I. (2017) Experimental assessment of aggregation principles in argumentation-enabled collective intelligence. ACM Transactions on Internet Technology, 17, 3, 1-21


Woolley, A. W., Aggarwal, I., and Malone, T. W., Collective intelligence in teams and organizations. In T. W. Malone and M. S. Bernstein (Eds.), Handbook of Collective Intelligence. Cambridge, MA: MIT Press, 2015


Aggarwal, I., Woolley, A. W., Chabris, C. F., and Malone, T. W. Cognitive diversity, collective intelligence, and learning in teams. Proceedings of Collective Intelligence 2015, Santa Clara, CA, June 2, 2015


Engel, D., Woolley, A. W., Jing, L. X., Chabris, C. F., & Malone, T. W. (2014) Theory of mind predicts collective intelligence. Proceedings of Collective Intelligence 2014, Cambridge, MA. (link: be7c69d0dd1766318263e3228d8108fdc7c2d3d2deca85f5f6760051b0a0574020Papersbe7c69d0dd1766318263e3228d8108fdc7c2d3d2deca85f5f6760051b0a057405CPaperbe7c69d0dd1766318263e3228d8108fdc7c2d3d2deca85f5f6760051b0a0574020106.pdf )


Awad, E., Bonnefon, J., Caminada, M., Malone, T., & Rahwan, I. (2017) Experimental assessment of aggregation principles in argumentation-enabled collective intelligence. ACM Transactions on Internet Technology, 17, 3, 1-21.


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