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The Master Algorithm

书名：The Master AlgorithmHowtheQuestfortheUltimateLearningMachineWillRemakeOurWorld
作者：PedroDomingos
译者：
ISBN：9780465065707
出版社：BasicBooks
出版时间：2015-9-22
格式：epub/mobi/azw3/pdf
页数：352
豆瓣评分： 7.9

书籍简介：

A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner–the Master Algorithm–and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.

作者简介：

Pedro Domingos is a professor of computer science at the University of Washington. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. A fellow of the Association for the Advancement of Artificial Intelligence, he lives near Seattle.

书友短评：

@ mz Recruiter 推荐的，很不适的读完了(还好和实际工作没关系！)。开头结尾有的观点只能用恶心来形容。非常不喜欢over selling(讨论人类小孩的学习能力之后介绍k-means 算法，seriously?), 虽然会让一些人激动，但能引起的反感远远大于这部分人激动的意义。而且有必要提master algorithm 么？有必要么？为什么要实现一个和人类大脑一样的算法呢？地球上已经七十多亿人了，这样的算法有什么用？算法有用就是因为它和人不一样。很难想象一个有着人类大脑一样学习能力的算法不会犯人类大脑的错误。不知这书的目标读者是什么人？感兴趣的话看公式不好吗？这个领域需要的是脚踏实地的介绍各种方法在哪些领域有用，哪些领域没用，为什么没用。不过现今这些问题即使研究者也很难回答。 @ 六八一零 Recruiter 推荐的，很不适的读完了(还好和实际工作没关系！)。开头结尾有的观点只能用恶心来形容。非常不喜欢over selling(讨论人类小孩的学习能力之后介绍k-means 算法，seriously?), 虽然会让一些人激动，但能引起的反感远远大于这部分人激动的意义。而且有必要提master algorithm 么？有必要么？为什么要实现一个和人类大脑一样的算法呢？地球上已经七十多亿人了，这样的算法有什么用？算法有用就是因为它和人不一样。很难想象一个有着人类大脑一样学习能力的算法不会犯人类大脑的错误。不知这书的目标读者是什么人？感兴趣的话看公式不好吗？这个领域需要的是脚踏实地的介绍各种方法在哪些领域有用，哪些领域没用，为什么没用。不过现今这些问题即使研究者也很难回答。 @ 所列门 Great general intro of five tribes of machine learning algorithms, also with some deeper discussion of coordinations of various learners. Though no defined master algorithm yet, this book provided useful insights of what potential master learners may like

书籍目录

  • Privacy is only one aspect of the larger issue of data sharing, and if we focus on it to the detriment of the whole, as much of the dabate to date has, we risk reaching the wrong conclusions. … When people have to trade off privacy against other benefits, as when filling out a profile on a website, the implied value of privacy that comes out is much lower than if you ask them abstract questions like “Do you care about your privacy?” But privacy debates are more often framed in terms of the latter. … Privacy is not a zero-sum game, even though it’s often treated like one. ––To share or not to share, and how and where
    —— 引自第263页
  • In the early days of AI, the common view was that computers would replace blue-collar workers before white-collar ones, because white-collar work requires more brains. But that’s not quite how things turned out. Robots assemble cars, but they haven’t replaced construction workers. On the other hand, machine-learning algorithms have replaced credit analysts and direct marketers. … The common theme is that narrowly defined tasks are easily learned from data, but tasks that require a broad combination of skills and knowledge aren’t. Most of your brain is devoted to vision and motion, which is a sign that walking around is much more complex than it seems; we just take it for granted because, having been honed to perfection by evolution, it’s mostly done subconsciously. ––A neural network stole…
    —— 引自第263页

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