✅ Fixed all forward dependency violations across modules 3-10
✅ Learning progression now clean: each module uses only previous concepts
Module 3 Activations:
- Removed 25+ autograd/Variable references
- Pure tensor-based activation functions
- Students learn nonlinearity without gradient complexity
Module 4 Layers:
- Removed 15+ autograd references
- Simplified Dense/Linear layers to pure tensor operations
- Clean building blocks without gradient tracking
Module 7 Spatial:
- Simplified 20+ autograd references to basic patterns
- Conv2D/BatchNorm work with basic gradients from Module 6
- Focus on CNN mechanics, not autograd complexity
Module 8 Optimizers:
- Simplified 50+ complex autograd references
- Basic SGD/Adam using simple gradient operations
- Educational focus on optimization math
Module 10 Training:
- Fixed import paths and simplified autograd usage
- Integration module using concepts from Modules 6-9 only
- Clean training loops without advanced patterns
RESULT: Clean learning progression where students only use concepts
they've already learned. No more circular dependencies!
- Add in-place activation functions (relu_, sigmoid_, tanh_, softmax_)
- Implement direct tensor modification to save memory (~50% reduction)
- Add comprehensive testing for correctness and memory verification
- Include performance profiling and comparison methods
- Add educational content on memory efficiency and production patterns
- Follow PyTorch convention for in-place operations (function_)
- Complete module to 100% with all functionality implemented
