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Practical Neural Networks in Python and MATLAB

Name: Practical Neural Networks in Python and MATLAB
Brand: Springer, Berlin
SKU: 9783032147455
Price: 1566 CZK
Availability: InStock

Autor: Chunwei Zhang, Tianpeng Li, Ying Dai, Li Sun, Ardashir Mohammadzadeh

Jazyk:

Vazba: Pevná

Vydavatel: Springer, Berlin

Dostupnost: Skladem u dodavatele

Odesíláme za 10-13 dnů

1 566 Kč

A Comprehensive Guide to Theory and Implementation.Bridging the gap between theory and practice with...

Informace o knize

Autor

Chunwei Zhang, Tianpeng Li, Ying Dai, Li Sun, Ardashir Mohammadzadeh

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2026

Stránek

146

EAN

9783032147455

Enbook ID

50043340

Vydavatel

Springer, Berlin

Hmotnost

385

Rozměry

155 x 235

Kompletní popis

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 requires 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

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