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Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena.
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This book provides advanced techniques for precision compensation and fault diagnosis of precision motion systems and rotating... Læs mere
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This text explores a diverse set of data science topics through a mathematical lens, helping mathematicians become acquainted with data science in... Læs mere
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Hybrid Genetic Optimization for IC Chip Thermal Control: with MATLAB® applications focuses on the detailed... Læs mere
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This book provides a complete overview on the deep learning applications and deep neural network architectures. It also gives an overview on most advanced future-looking fundamental research in deep learning application in artificial intelligence.
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Transformers are becoming a core part of many neural network architectures, employed in a wide range of applications such as NLP, Speech Recognition, Time... Læs mere
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This title will help business users and AI practitioners to identify good AI projects and ensuring they are successfully delivered to support their business... Læs mere
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Focusing on how visual information is represented, stored and extracted in the human brain, this book uses cognitive neural modeling in order to show how visual information is represented and memorized in the brain.
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This book brings together examples from the “Innovative Ecosystem with Artificial Intelligence for Andalusia... Læs mere
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AI by Design introduces AI and its importance to our future. Exploring behavioural psychology, economics, and real-life and historical... Læs mere
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Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the... Læs mere
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Uncertainty is certain to be found in structural engineering, making it crucial to structure design. This book covers three competing philosophies behind structural safety and reliability: probabilistic analysis, fuzzy-sets based treatments, and the convex approach.