- Taschenbuch: 156 Seiten
- Verlag: O'Reilly and Associates; Auflage: 1 (23. März 2012)
- Sprache: Englisch
- ISBN-10: 1449314635
- ISBN-13: 978-1449314637
- Größe und/oder Gewicht: 17,8 x 0,9 x 23,3 cm
- Durchschnittliche Kundenbewertung: 2 Kundenrezensionen
- Amazon Bestseller-Rang: Nr. 7.550 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
Think Complexity: Complexity Science and Computational Modeling (Englisch) Taschenbuch – 23. März 2012
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Über den Autor und weitere Mitwirkende
Allen Downey is an Associate Professor of Computer Science at the Olin College of Engineering. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master s and Bachelor s degrees from MIT."
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What I liked, however, is the way Allen presents the material. He tries to show you different aspects of the development process and refers not only to computer science but to philosophy and mathematics as well. Even if you won't be able to solve all the presented puzzles it is still worth getting through the book.
Few remarks regarding what I really liked in the book. First of all, Allen provides you with lots of references. So, if you are interested in particular topic, you have plenty of sources to start with. Secondly, Allen provides you with references to Wikipedia very often. This is not regarded usually as a good source among 'university like people', however I like this kind of approach a lot.
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Allen B. Downey's well-written new book can help you dive into complexity science and improve your Python skills along the way. It's not just another hello-world, learn-to-program-in-Python text.
"This book," Downey states, "is about data structures and algorithms, intermediate programming in Python, computational modeling, and the philosophy of science." Hello, NEW world.
His new work, he adds, sprang out of a blending of "boredom and fascination: boredom with the usual presentation of data structures and algorithms and fascination with complex systems. The problem with data structures is that they are often taught without a motivating context; the problem with complexity science is that it usually is not taught at all."
Complexity science is the scientific study of complex systems - which can be anything from computer networks to the human brain, global markets, ecosystems, metropolitan areas, space shuttles, ant trails, and so forth. Complexity science is practiced "at the intersection of mathematics, computer science, and natural science," Downey says.
How does "the philosophy of science" fit into Downey's book? "Think Complexity" offers "experiments and results [that] raise questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and epistemology."
Downey's new work "picks up where Think Python left off" and is intended to appeal to the "broad intellectual curiosity" of software engineers and their "drive to expand their knowledge and skills." There are case studies, exercises, code samples and even mini-lessons within the exercises.
So, before you jump into this book, be sure you are reasonably competent at Python programming and are open to some wide-ranging challenges.
Students at Olin College, where the author is a computer science professor, wrote the case studies for this book. The case studies then were edited by Downey and his wife and reviewed by other Olin faculty members. Appendix A of this book contains a call for readers to submit additional case studies: "Reports that meet the criteria [explained in the appendix] will be published in an online supplement to this book, and the best of them will be included in future print editions."
This might be an offer -- and a Python challenge -- you can't refuse.
It was interesting how the author organized the idea shift in scientific thinking of the complexity science. If one is familiar with the works like Malcolm Gladwell in "Blink", "Outliers" or similarly in "Freakonomics" one can clearly related to the method of using simulation-based computational model to solve problems that are non-linear with large composite, many-to-many elements. Many of the TED talks I have seen also employed this line of method in arriving at their respective conclusions.
The middle section of the book introduced various models and approaches into solving complex problems. I absolutely love the fact that the theories were broken down into small pieces of problems that can be illustrated by small Python programs. Of the examples, the sections on Dijkstra algorithm and scale-free networks were the most interesting to me. As network engineer whom have dealt with OSPF and IS-IS on regular basis, I never thought it was possible to simulate the algorithm via Python. That was a treat. I also have some ideas inspired by the scale-free networks section that I feel I can apply to work.
Bottom line, if Professor Downey ever opens an online class for "Think Complexity" either synchronized or on UDemy I would sign up in a heartbeat. Cheers.
So when I concede myself the luxury of buying a "real" book, I expect it to be something that I can enjoy sitting on a sofa or in bed, as a stand alone item.
This book is certainly an interesting read for the topics it examines, however it completely fails on my requirements. There is not a single page in which the author is not asking the reader to go check a wikipedia page, download a scientific paper or go examine a piece of code available on the book's companion website.
This leaves the reader two choices: either do what the author is asking, sacrificing what should have been a reading session for yet another go of clicks and scrolls or (what i did) just ignore the suggestions. This will obviously make it more difficult to follow the line of thought, especially because the author many times is posing questions which have no answer in the book itself. So if you don't do the homework you never get the answer!
Overall the continuous referencing to external resources has left the feeling in me that this piece of work could have been a stimulating and interesting one if only the author had put in it the extra effort to make it a self standing reading. He could still have provided links to external resources, but only as optional.
In the end I don't recommend it unless you are really committed to following the author's path, which may be more doable for a college course type of reader than for a casual one like myself.
This book gives relatively straightforward programs in the Python Language which explain and illustrate phenomena such as Conway's "Game of Life", Wolfram's Cellular Automata experiments, and fractal graphics which can be run on a experimenter's own PC. Moreover, this book invites the reader to design their own experiments which may be published in a subsequent edition of the book and which give the real possibility of participating in new science to a moderately skilled home experimenter.
The book also importantly provides new motivation to one of the most basic skills of computer science by providing a way through which relatively simple data structures can yield important and surprising results in a variety of new science.
--Ira Laefsky, MSE/MBA
Home Experimenter formerly on the Senior Consulting Staff of Arthur D. Little, Inc. and Digital Equipment Corporation
I cannot comment on the difficulty of the mathematics in this book since, as a graduate student in mathematics, it is all very elementary to me. However I would expect a good solid calculus course should be enough. If not, the Internet has a well of resources on most of the topics covered.
If you are still unsure, the author has the full pdf of the book available to download on his website so you can check it out yourself.