A truly intelligent system is one that can learn on its own. This article delves into more-complex neural networks that underpin deep learning — the technology that is revolutionizing artificial intelligence.
Chemical engineers touch billions of lives. In part, their success is due to their ability to leverage emerging technologies from other fields. Although artificial intelligence (AI) is still in its infancy, chemical engineers are already leveraging AI across a diverse array of applications. Deep learning is an approach to AI that is powering game-changing solutions in every domain, and it will be a formidable tool in the chemical engineer’s toolbox.
The CEP article “Introduction to Deep Learning: Part 1” (June 2018, pp. 22–29) introduced foundational concepts of deep learning, including artificial neurons, activation functions, artificial neural networks, and the backpropagation algorithm. Here, Part 2 introduces the more-complex structures and approaches that underlie artificial neural networks in current practice, and briefly touches on socioeconomic aspects of AI.
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