机器学习的未来:终极算法探索

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"算法设计(英文版)(高清).pdf" 本书《算法设计》深入探讨了机器学习领域的核心概念和方法,旨在揭开这一领域的神秘面纱,并向读者展示机器学习如何重塑我们的世界。作者Pedro Domingos通过清晰易懂的方式阐述了机器学习的科学原理,不仅对非技术人员友好,也为专业人士提供了新颖而深刻的见解。 在书中,Domingos讨论了机器学习作为预测分析在商业和社会中的关键作用,它已经并且将继续深刻地影响我们的生活和工作。他引用了Thomas Davenport的观点,强调机器学习已经成为并将继续成为我们生活的重要部分,而Domingos的著作正是为了解释这个领域,使人们能够更好地理解和适应这一变化。 此外,Eric Siegel,作为Predictive Analytics World的创始人和《Predictive Analytics》一书的作者,对这本书给予了高度评价。他认为,《算法设计》成功地将深奥的科学概念呈现给非专业人士,同时也为专家们提供了对未来研究方向的独到见解,这是一本罕见的兼具深度和广度的书籍。 部分内容中提及的"The Master Algorithm",暗示了Domingos可能在书中探讨了寻找一种通用学习算法的主题,这种算法可以统一各种机器学习方法,从而实现更高效、更智能的学习。书中可能涵盖了监督学习、无监督学习、强化学习等不同类型的机器学习算法,以及它们在模式识别、自然语言处理、图像识别等领域的应用。 此外,书中可能还涉及了深度学习、神经网络、决策树、支持向量机等关键技术,并探讨了这些技术如何协同工作,以构建更加智能的系统。Domingos可能会讨论这些算法的优点、限制以及如何优化它们,以便更好地解决实际问题。 《算法设计》不仅提供了一个全面的机器学习教程,还引导读者思考机器学习的未来潜力及其对社会的影响。这本书对于那些想要深入了解机器学习基础和前沿发展的人来说,是一份宝贵的资源。
2018-09-19 上传
英文版 算法设计 Preface Algorithmic ideas are pervasive, and their reach is apparent in examples both within computer science and beyond. Some of the major shifts in Internet routing standards can be viewed as debates over the deficiencies of one shortest-path algorithm and the relative advantages of another. The basic notions used by biologists to express similarities among genes and genomes have algorithmic definitions. The concerns voiced by economists over the feasibility of combinatorial auctions in practice are rooted partly in the fact that these auctions contain computationally intractable search problems as special cases. And algorithmic notions aren’t just restricted to well-known and longstanding problems; one sees the reflections of these ideas on a regular basis, in novel issues arising across a wide range of areas. The scientist from Yahoo! who told us over lunch one day about their system for serving ads to users was describing a set of issues that, deep down, could be modeled as a network flow problem. So was the former student, now a management consultant working on staffing protocols for large hospitals, whom we happened to meet on a trip to New York City. The point is not simply that algorithms have many applications. The deeper issue is that the subject of algorithms is a powerful lens through which to view the field of computer science in general. Algorithmic problems form the heart of computer science, but they rarely arrive as cleanly packaged, mathematically precise questions. Rather, they tend to come bundled together with lots of messy, application-specific detail, some of it essential, some of it extraneous. As a result, the algorithmic enterprise consists of two fundamental components: the task of getting to the mathematically clean core of a problem, and then the task of identifying the appropriate algorithm design techniques, based on the structure of the problem. These two components interact: the more comfortable one is with the full array of possible design techniques, the more one starts to recognize the clean formulations that lie within messy problems out in the world. At their most effective, then, algorithmic ideas do not just provide solutions to well-posed problems; they form the language that lets you cleanly express the underlying questions. The goal of our book is to convey this approach to algorithms, as a design process that begins with problems arising across the full range of computing applications, builds on an understanding of algorithm design techniques, and results in the development of efficient solutions to these problems. We seek to explore the role of algorithmic ideas in computer science generally, and relate these ideas to the range of precisely formulated problems for which we can design and analyze algorithms. In other words, what are the underlying issues that motivate these problems, and how did we choose these particular ways of formulating them? How did we recognize which design principles were appropriate in different situations? In keeping with this, our goal is to offer advice on how to identify clean algorithmic problem formulations in complex issues from different areas of computing and, from this, how to design efficient algorithms for the resulting problems. Sophisticated algorithms are often best understood by reconstructing the sequence of ideas—including false starts and dead ends—that led from simpler initial approaches to the eventual solution. The result is a style of exposition that does not take the most direct route from problem statement to algorithm, but we feel it better reflects the way that we and our colleagues genuinely think about these questions.