Tianpeng Li – författare
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2 produkter
2 produkter
Inbunden, Engelska, 2026
784 kr
Skickas inom 10-15 vardagar
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 medical diagnostics (e.g., diabetes classification), time-series forecasting (e.g., air quality prediction), system identification, natural language processing, and more. These examples provide complete pipelines from data preprocessing and model training to evaluation and visualization.This Book is Ideal For:University students and researchers in Computer Science, Artificial Intelligence, Engineering, and related fields.R&D engineers and scientists working in algorithm development, data analysis, and intelligent systems.Any practitioner seeking a thorough, hands-on understanding of neural networks with the flexibility to work in both Python and MATLAB environments.In essence, Practical Neural Networks in Python and MATLAB serves as an invaluable companion for anyone looking to deepen their expertise in neural networks. It is more than a textbook; it is a practical toolkit designed to accelerate your research, enhance your projects, and provide a clear, comprehensive reference for the key architectures and algorithms shaping the field of AI today.
E-bok
Engelska, 2026978 kr
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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 medical diagnostics (e.g., diabetes classification), time-series forecasting (e.g., air quality prediction), system identification, natural language processing, and more. These examples provide complete pipelines from data preprocessing and model training to evaluation and visualization.This Book is Ideal For:University students and researchers in Computer Science, Artificial Intelligence, Engineering, and related fields.R&D engineers and scientists working in algorithm development, data analysis, and intelligent systems.Any practitioner seeking a thorough, hands-on understanding of neural networks with the flexibility to work in both Python and MATLAB environments.In essence, Practical Neural Networks in Python and MATLAB serves as an invaluable companion for anyone looking to deepen their expertise in neural networks. It is more than a textbook; it is a practical toolkit designed to accelerate your research, enhance your projects, and provide a clear, comprehensive reference for the key architectures and algorithms shaping the field of AI today.