What you learned
1. Training robust neural networks
- PyTorch and OOP
- Optimizers
- Vanishing and exploding gradients
2. Images and convolutional neural networks
- Handling images with PyTorch
- Training and evaluating convolutional networks
- Data augmentation
3. Sequences and recurrent neural networks
- Handling sequences with PyTorch
- Training and evaluating recurrent networks (LSTM and GRU)
4. Multi-input and multi-output architectures
- Multi-input models
- Multi-output models
- Loss weighting
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