Learn Before
Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Learners will master the foundational concepts behind two landmark deep learning architectures: Transformers and Deep Residual Networks. They will gain a comprehensive understanding of self-attention mechanisms, sequence-to-sequence modeling, identity mappings, and deep feature representations. Through empirical analysis and structural comparison, participants develop the skills to design, train, and evaluate scalable models across language and vision domains.
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Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
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Learn After
Ch.1 Transformer Architecture and Components - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.2 Transformer Training and Evaluation - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.3 Deep Residual Network Architecture - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor
Ch.4 Residual Network Experiments and Applications - Foundational Deep Learning Architectures: Transformers and Residual Networks @ University of Michigan - Ann Arbor