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Course content
Sections:
5
•
Activities:
0
•
Resources:
18
- 1 Introduction to Learning PyTorch from Basics to Advanced Complete Training
- 2 Introduction to PyTorch
- 3 Getting Started with PyTorch
- 4 Working with Tensors
- 5 Autograd and Dynamic Computation Graphs
- 6 Building Simple Neural Networks
- 7 Loading and Preprocessing Data
- 8 Model Evaluation and Validation
- 9 Advanced Neural Network Architectures
- 10 Transfer Learning and FineTuning
- 11 Handling Complex Data
- 12 Model Deployment and Production
- 13 Debugging and Troubleshooting
- 14 Distributed Training and Performance Optimization
- 15 Custom Layers and Loss Functions
- 16 Researchoriented Techniques
- 17 Integration with Other Libraries
- 18 Contributing to PyTorch and Community Engagement