Neural Network Algorithms and Their Engineering Applications

Neural Network Algorithms and Their Engineering Applications by Chao Huang, Hailong Huang, and Yiying Zhang is a practical, forward-looking guide that bridges theory and real-world engineering. Designed for professionals and advanced students, this book distills complex neural network principles into clear algorithms and actionable design patterns for applied systems.

Start with a compelling overview of contemporary neural architectures and move quickly into algorithmic strategies that solve concrete engineering problems—signal processing, control systems, predictive maintenance, image and speech recognition, and industrial automation. Each chapter pairs mathematical rigor with implementation-minded explanations, making it easy to translate models into robust applications across sectors.

Why this book matters: it focuses on engineering constraints—computational efficiency, robustness, interpretability, and deployment on embedded and edge devices—so readers can develop solutions that work in production, not just in research labs. Practical examples, pseudo-code, and comparative analyses equip readers to choose and tune algorithms for their specific contexts.

Ideal for machine learning engineers, systems designers, researchers, and graduate students, this title supports professionals across industries and regions who need dependable, scalable neural solutions. Whether you’re prototyping an algorithm for a startup, optimizing models for manufacturing, or teaching applied AI, this book is a go-to resource.

Elevate your engineering toolkit with pragmatic guidance from leading authors in the field. Add Neural Network Algorithms and Their Engineering Applications to your library and start building neural solutions that meet real-world demands.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.