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Book Overview & Buying
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Table Of Contents
Hands-On Artificial Intelligence for IoT - Second Edition
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Python, with its rich scientific computing ecosystem, provides several excellent frameworks that streamline the implementation and experimentation with EAs. In this section, we’ll delve into two popular choices: PyGAD and distributed EAs in Python (DEAP).
PyGAD is a user-friendly library built on top of NumPy, emphasizing simplicity for both practitioners and researchers. Its design allows for easy customization of various EA components. It supports a wide range of genetic operators (selection, crossover, mutation). It also provides flexibility to create custom operators. It integrates seamlessly with ML libraries such as PyTorch, TensorFlow, and Keras. It also offers built-in visualization tools for analysis. It is best suited when ease of use, flexibility in defining operators, and integration with other Python libraries are important considerations.
DEAP is a mature and...