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Deep Learning — CS 7643
CNNs, RNNs and attention implemented from scratch in PyTorch.
The problem
Georgia Tech's CS 7643 implements the deep-learning curriculum hands-on rather than as a survey. Each homework rebuilds a foundational architecture from scratch in PyTorch, then benchmarks the from-scratch version against the pretrained baseline.
Who this is for
GT prospective students, anyone deciding whether the OMSCS deep-learning track is worth taking.
Architecture
- CNNs
- Built from conv / pool / batchnorm primitives for image classification.
- RNNs
- Vanilla RNN → LSTM for sequence modelling.
- Attention
- Scaled dot-product attention from scratch, then transfer-learning on top.
Request / data flow
- 01Read the paper.
- 02Implement the primitive in raw PyTorch.
- 03Train on the assigned dataset.
- 04Compare against the pretrained baseline — explain the gap.
Stack
PyTorchCNNRNNAttentionTransfer Learning