PyTorch Fundamentals
- Tensoroperationen und Autograd
- Datenverwaltung und DataLoader
- GPU-Beschleunigung
- Grundlegende Neural Network Module
- Custom Layer Implementation
- Feed-Forward Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- Loss Functions und Optimierer
- Batch Normalization und Dropout
- Transformer-Architekturen
- Attention Mechanismen
- Generative Adversarial Networks
- Auto-Encoder
- Transfer Learning
- Hyperparameter-Tuning
- Learning Rate Scheduling
- Model Pruning
- Quantisierung
- Distributed Training
- Model Serialization
- TorchScript
- ONNX Export
- Mobile Deployment
- Cloud Integration
- Code-Organisation
- Debugging Strategien
- Performance Optimierung
- Memory Management
- Testing und Validation
- Bildklassifizierung
- Objekterkennung
- Sequenzvorhersage
- Textgenerierung
- Style Transfer