erdEducation Reform and Development2652-53642652-5372Bio-Byword Scientific Publishing Pty. Ltd.10.26689/erd.v8i8.15229ArticleJupyter-Based Course Resource Reconstruction and Interactive Teaching Design for Deep Learninghttps://artdesignp.com/journal/erd/8/8/10.26689/erd.v8i8.15229YangLe,LiuLin2026882026-09-08The Deep Learning course combines substantial theoretical content with intensive practical work. In conventional instruction, theoretical explanations, program code, and execution results are often presented separately, making it difficult for students to establish connections among model principles, code implementation, and application tasks. Using Jupyter Notebook as the instructional environment, this study reconstructs existing lecture slides, programming examples, and laboratory materials into coherent, executable teaching units that integrate theoretical explanations, PyTorch code, execution feedback, and learning tasks. Project-based learning, self-directed inquiry, collaborative learning, tiered tasks, and multidimensional assessment are embedded in the design. A convolutional neural network teaching unit is used to illustrate the organization of convolution principles, code-based verification, network construction, image classification, and parameter exploration. The paper further proposes an assessment framework encompassing knowledge mastery, practical competence, learning processes, collaborative performance, and learning experience, together with a mechanism for continuously refining resources in response to teacher and student feedback. The proposed design may inform the integration of theoretical instruction and programming practice in deep learning and related artificial intelligence courses.Jupyter Notebook, Deep learning course, Course resource reconstruction, Interactive teaching, Project-based learning

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