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Free PyTorch course

Advanced Deep Learning with PyTorch

Module 05

Optimization, Initialization, Normalization, and Regularization

teach why training can fail and how to diagnose it.

Units

  1. Unit 05.00: 05.00 Learning rate and optimizer choice
  2. Unit 05.01: 05.01 SGD, momentum, Adam, and practical trade-offs
  3. Unit 05.02: 05.02 Initialization and vanishing/exploding gradients
  4. Unit 05.03: 05.03 Batch normalization and layer normalization intuition
  5. Unit 05.04: 05.04 Dropout, weight decay, and early stopping
  6. Unit 05.05: 05.05 Learning curves and instability signals
  7. Unit 05.06: 05.06 Project step: training diagnostics report

Module work