<![CDATA[The deep learning curriculum is designed to provide student with a strong theoretical foundation as well as practical skills in the development and implementation of articial intelligence models. A modular approach is used to cover core material such as neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transfer learning techniques. The curriculum also focuses on applications of deep learning in various fields, including computer vision, natural language processing (NLP), and big data analysis. The primary goal of the curriculum is to encourage innovation through research-based projects relevant to real-world problems, such as pattern recognition, sentiment analysis, and data-driven predictions. In addition, this curriculum instills awareness of the importance of ethical and social responsibility aspects in the development of AI technology. With a holistic approach, the deep learning curriculum is expected to be able to produce graduates who are competent, innovative and ready to compete in the era of industrial revolution 4.0.]]>
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