Deep Dive into Graph Neural Networks (GNN) Tutorial
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PyTorch Deep Learning and Graph Neural Networks: Foundations (Vol 1)
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Introduction to Graph Neural Networks (Turing Publishing, DeepSeek Tutorial)
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Gene Cheung Graph Spectral Image Processing
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Graph spectral image processing is the study of imaging data from a graph frequency perspective. Modern image sensors capture a wide range of visual data including high spatial resolution/high bit-depth 2D images and videos, hyperspectral images, light field images and 3D point clouds. The field of
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Dive into Deep Learning
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Jade Carter Neural Networks Beginnings
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The book is an excellent resource for those who want to get acquainted with the basics of neural networks and their application in life. The book explains in detail what a neuron is and how it works in a neural network, what weights and biases are, how a neuron makes decisions, and how a neural
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Cirrincione Giansalvo Neural-Based Orthogonal Data Fitting. The EXIN Neural Networks
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The presentation of a novel theory in orthogonal regression The literature about neural-based algorithms is often dedicated to principal component analysis (PCA) and considers minor component analysis (MCA) a mere consequence. Breaking the mold, Neural-Based Orthogonal Data Fitting is the first
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Antonios K. Alexandridis Wavelet Neural Networks
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A step-by-step introduction to modeling, training, and forecasting using wavelet networks Wavelet Neural Networks: With Applications in Financial Engineering, Chaos, and Classification presents the statistical model identification framework that is needed to successfully apply wavelet networks as
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Александр Чичулин Neural networks guide. Unleash the power of Neural Networks: the complete guide to understanding, Implementing AI
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Explore the fascinating world of neural networks in this comprehensive and beginner-friendly guide. Learn how these powerful algorithms mimic the human brain, understand their components, and implement them using Python. Discover applications, ethical considerations, and stay updated with the
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Madan Gupta Static and Dynamic Neural Networks
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Provides comprehensive treatment of the theory of both static and dynamic neural networks. * Theoretical concepts are illustrated by reference to practical examples Includes end-of-chapter exercises and end-of-chapter exercises. *An Instructor Support FTP site is available from the Wiley editorial department.
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Simon Haykin Kalman Filtering and Neural Networks
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State-of-the-art coverage of Kalman filter methods for the design of neural networks This self-contained book consists of seven chapters by expert contributors that discuss Kalman filtering as applied to the training and use of neural networks. Although the traditional approach to the subject is
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Александр Чичулин Neural Networks for Big Money
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Unleash the potential of neural networks for financial success! This book equips readers of all ages with the knowledge and strategies needed to effectively use neural networks in business.From understanding the basics to practical application! Learn how to make big money using best practices. Gain
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