Applied Neural Networks in the AI Era From Theory to Real-World Impact

In the era of artificial intelligence (AI), applied neural networks transition from theoretical constructs to powerful tools driving innovation across sectors. Neural networks can learn patterns, make predictions, and adapt to complex data. From powering image and speech recognition systems to enabling autonomous vehicles and enhancing medical diagnostics, their impact is continually expanding. Advances in computational power, big data, and algorithmic design accelerate this transformation, making neural networks critical to AI applications. As these models become integrated into everyday technologies, further research into their design, limitations, and ethical implications becomes pivotal. Applied Neural Networks in the AI Era: From Theory to Real-World Impact explores the integration of intelligent technologies into neural networks. It examines the application of neural networks in various sectors, including transportation, medicine, computing, etc. This book covers topics such as biology, cloud computing, and smart robotics, and is a useful resource for engineers, business owners, academicians, researchers, and computer scientists.

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