Deep Learning: From Intuition to Deployment

From neural networks to Transformers: tensors, MLPs, forward propagation, gradient and backpropagation, optimization, initialization, regularization, then CNNs, RNN/LSTM/GRU, attention, embeddings and generative models — through to MLOps deployment and ethics. Five PyTorch labs, a defensible final project, nine modern deep dives.

AdvancedAOA-DL-201
01

Understanding Deep Learning

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02

Building a Network

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03

Training the Network

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Specialized Architectures

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From Model to System

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Hands-on Labs (5 Labs)

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Final Project

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Modern Deep Dives

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Appendices: Quiz Bank, Glossary and References

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