ga642381/ML2021-Spring
**Official** 李宏毅 (Hung-yi Lee) 機器學習 Machine Learning 2021 Spring
Comprises 15 hands-on assignments covering core ML paradigms: supervised learning (regression, classification), deep architectures (CNNs, Transformers, GANs, autoencoders), and advanced topics (adversarial robustness, domain adaptation, reinforcement learning, meta-learning). Each homework includes starter code and theoretical foundations, designed to be completed with PyTorch while complemented by lecture videos explaining underlying concepts.
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