virginiakm1988/ML2022-Spring

Comprehensive coverage of modern deep learning architectures and techniques spanning supervised learning (regression, classification, CNNs) through advanced topics like transformers, GANs, BERT, reinforcement learning, and meta-learning. Each homework pairs Jupyter notebooks with slide decks and accompanying lecture videos, enabling self-paced study with both theoretical foundations and practical implementation. Targets PyTorch-based workflows for computer vision and NLP tasks, with emphasis on interpretability, robustness, and model optimization techniques.

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