9783032147455 - practical neural networks in python and matlab von zhang, chunwei; li, tianpeng; dai, ying; sun, li; mohammadzadeh, ardashir (2 Ergebnisse)
Practical Neural Networks In Python And Matlab
Zhang, Chunwei Li, Tianpeng Dai, Ying Sun, Li Mohammadzadeh, Ardashir
- Hardcover
Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books
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EUR 112,91
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Hardcover. Zustand: Brand New. 178 pages. 6.30x0.63x9.49 inches. In Stock.
- Hardcover
Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH
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EUR 78,17
EUR 62,20 VersandVersand von Deutschland nach USAAnzahl: 1 verfügbar
Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - A Comprehensive Guide to Theory and Implementation.Bridging the gap between theory and practice with this extensive guide to neural networks, featuring parallel implementations in both Python and MATLAB.Navigating the complex landscape of neural networks re…quires not only a firm grasp of theoretical foundations but also the practical skills to implement them effectively. Practical Neural Networks in Python and MATLAB is designed to be a definitive resource, offering a unique dual-language approach to mastering these powerful models.Key Features:A Dual-Language, Integrated Approach: This book provides a side-by-side exploration of neural networks in both Python and MATLAB. This methodology allows you to leverage Python's rich deep learning ecosystem (TensorFlow, Keras, PyTorch) and MATLAB's specialized toolboxes, giving you the flexibility to work within your preferred environment or across different project requirements.Comprehensive Coverage of Algorithms and Architectures: Move beyond basic backpropagation. The text provides a systematic review of fundamental and advanced training algorithms, including Gradient Descent, Newton's Method, Levenberg-Marquardt, Recursive Least Squares (RLS), and metaheuristics like Genetic Algorithms and Particle Swarm Optimization. Furthermore, it offers a detailed survey of over 25 major neural network architectures, from foundational Perceptrons and Feedforward Networks to advanced systems like CNNs, RNNs (LSTM, GRU), Autoencoders, GANs, and Deep Belief Networks.Practical, Code-Oriented Learning: Each concept and architecture is accompanied by ready-to-run code examples. This practical focus ensures that you can immediately translate theoretical understanding into functional code, experiment with parameters, and adapt the implementations to your own unique challenges.Real-World Application and Case Studies: The learning is grounded in practicality through diverse case studies across multiple domains. You will find applications in medi.
