Think Complexity
Whether you're an intermediate-level Python programmer or a student of computational modeling, you'll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.
In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform; Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines; Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata; Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism.
Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
Table of contents
- Preface
- Chapter 1
- Chapter 2
- Chapter 3
- Chapter 4
- Chapter 5
- Chapter 6
- Chapter 7
- Chapter 8
- Chapter 9
- Chapter 10
- Chapter 11
- Chapter 12
- A Reading list
| Pages : | 228 |
| Size : | 6.6 MB |
| File type : | |
| Downloads: | 65 |
| Created: | 2022-02-03 |
| License: | CC BY-NC-SA |
| Author(s): | Allen Downey |
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