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About Modeling Complex Processes Through AI
This book is an introduction to nature-analogous techniques and related formal methods. For each technique, application examples are provided. It covers cellular automata and Boolean networks, evolutionary algorithms, as well as simulated annealing, fuzzy methods, neural networks, and finally hybrid systems, i.e., combinations of various techniques. Based on the theory of complex dynamic systems, theoretical foundations are also presented, and the similarities of these seemingly very heterogeneous techniques are pointed out. The edition has been revised and expanded with current trends such as ChatGPT. The current developments under the umbrella term “Artificial Intelligence” are rapid and hardly manageable. While the (hybrid) models we described were a rarity a few years ago, they are now a matter of course. The coupling of methods from Artificial Intelligence (AI), Artificial Life (AL), and Machine Learning (ML) as well as Deep Learning (DL) is currently too diverse to capture the increasing complexity or to process the large amounts of data. The students selected a very challenging problem as well as a model, without prior knowledge in neural networks, deep learning libraries, or cloud programming; without ChatGPT, they probably would not have dared the adventure and thus would not have been able to gain valuable experience. This also applies to the students who programmed a CNN in Python with the help of ChatGPT.
Detailed Information
| Author: | Christina Kluver |
|---|---|
| Pages: | 374 |
| Language: | English |
| File Size: | 15 |
| Format: | |
| Price: | FREE |
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