基於深度學習之影像辨識

課程大綱

第1週 Introduction to AI, Machine Learning, Deep Learning,LLM,VLM 第2週 Image Learning Basics (1) (Classification, Clustering, Forward & Backprop, and Loss function) 第3週 Image Learning Basics (2) (Classification, Clustering, Forward & Backprop, and Loss function) 第4週 Introduction of Implementation tools and practices (#TA) /Implementation of Distribution-based loss function (#TA)繳交分組名單 第5週 Deep Learning with CNN Techniques: Dropout, BatchNorm, Transformer, Multi-Head Attention 第6週 Neural Network implementation with PyTorch on Image classification (#TA) 第7週 Basics of Neural Networks: Classification, Localization and Segmentation 第8週 Midterm Examination 填寫期末project題目 第9週 Introduction of NN model(YOLO V5~YOLO V11) 第10週 YOLO Implementation with PyTorch: Object Detection and Segmentation (#TA) 第11週 Generative Adversarial Networks (GANs) & Super Resolution 第12週 GANs and Super Resolution: Architecture, Training and Implementation (#TA) (Optional) 第13週 Case study I 第14週 Case Study II 第15週 Advanced Deep Learning: Lightweight Models and Model Design Techniques 第16週 Advanced Neural Methods: Multi-Spectral, Multi-Models Implementation for Final Project (#TA)