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Training deep neural networks is as much about preventing overfitting and navigating complex loss landscapes as it is about building the right architecture. This lesson covers the key regularisation techniques and optimisation strategies that make deep learning work in practice.
Deep neural networks have millions of parameters — far more than the number of training examples in most datasets. This gives them enormous capacity to memorise the training data rather than learning generalisable patterns. Regularisation is the set of techniques used to prevent overfitting and improve generalisation.
| Symptom | Diagnosis | Solution |
|---|---|---|
| Training loss low, validation loss high | Overfitting | Apply regularisation |
| Both losses high | Underfitting | Increase model capacity or train longer |
| Both losses low and similar | Good generalisation | Model is well-regularised |
Dropout (Srivastava et al., 2014) randomly sets a fraction of neurons to zero during each training step. This prevents neurons from co-adapting and forces the network to learn redundant representations.
import torch.nn as nn
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