具体描述
Allen B. Downey拥有加州大学伯克利分校的计算机科学博土学位和MIT的硕士与学土学位，现任美国欧林工程学院计算机科学系的教授，曾经在威尔斯利大学、科尔比学院和加州大学伯克利分校教授计算机科学相关的课程。曾经担任 Google的访问科学家，对复杂性科学和Python十分着迷，有较为深入的研究和丰富的实践经验。
Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide. Whether you're an intermediate-level Python programmer, or a student of computational modeling, you'll examine data structures, complexity science, and other fascinating topics through a series of exercises, easy-to-understand explanations, and case studies. Think Complexity presents features that make Python such a simple and powerful language. Author Allen Downey provides code to help you get started, along with a solution for each exercise. With this book, you will: Work with graphs and graph algorithms, NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables. Discover complexity science, the field that studies abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines. Explore the philosophy of science through the models and results in this book about the nature of scientific laws, theory choice, and realism and instrumentalism, and more.
用户评价
##《Think Python》引流过来的，是很浅显的入门书，胜在广而不胜在深，不过对于现阶段的我很有用，一是入门了networkx，如果不是为了测试代码我估计都不会去配置这个模块，后续做了不少图论的实验，多个工具；二是启发我结合pagerank算法研究一个新的课题，现在开了个头。练习没有完全跟做，另外代码部分有些错误，比较明显。
评分 评分##作者在前言中交待得非常清楚了，这其实是他在欧林学院讲授算法和数据结构这门课程的讲义。如果让我给这本讲义提炼个摘要关键词的话，我觉得可能是：图，网络，演化，模拟，复杂性。毕竟是从传统的算法课程脱胎而来，图几乎是讲义中唯一还和传统算法沾边的地方（还略涉及到一点...
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