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深度學習於化工過程 (Deep Learning for Chemical Processes)
Artificial Intelligence (AI) has made remarkable progress over the past decade. The long-standing goal of developing intelligent systems that can think and act like humans—only faster and more accurately—is quickly becoming a reality. One of the most transformative advancements in this area is deep learning (DL), which has become a central focus in process systems engineering, attracting significant research and industry attention. DL is a rapidly evolving field, delivering outstanding results in tasks that were once dominated by human expertise.
In this course, we will explore the perceptron and other artificial neurons, which form the foundational building blocks of deep neural networks—the driving force behind the deep learning revolution. We will study fully connected feedforward networks and convolutional networks, applying them to solve practical industrial chemical engineering problems, such as handling high-dimensional data or diagnosing process faults. The course will cover essential deep learning components, including perceptrons, deep neural networks (DNNs), recurrent neural networks (RNNs), and popular deep learning frameworks. It will progressively build towards more advanced architectures, such as attention mechanisms, transformer models, and GPT systems.