Vijay Janapa Reddi
|
13ac7ee885
|
✨ Complete comprehensive capstone README rewrite
🎯 Major improvements to 16_capstone module documentation:
📚 Enhanced Structure:
- Updated to reflect actual 14-module progression (not 15)
- Celebrates complete ML framework students built
- Shows concrete working code examples using TinyTorch components
🚀 5 Specialized Tracks:
1. Performance Ninja - Speed/memory optimization, GPU acceleration
2. Algorithm Architect - Modern ML algorithms, Vision Transformers
3. Systems Engineer - Production infrastructure, distributed training
4. Benchmarking Scientist - Scientific framework comparison
5. Developer Experience Master - Debugging tools, visualization
⚡ Professional Framework:
- 4-phase timeline: Analysis → Implementation → Optimization → Evaluation
- Concrete project examples with code samples for each track
- Clear success criteria and measurable goals
- Comprehensive deliverables structure (Technical Report, Code, Analysis, Demo)
- Pro tips for framework engineering success
🎓 Outcome: Transforms basic optimization into comprehensive framework
engineering specialization that demonstrates production ML systems mastery
|
2025-07-18 02:07:30 -04:00 |
|
Vijay Janapa Reddi
|
59d58718f9
|
refactor: Implement learner-focused module progression with better naming
✅ Renamed modules for clearer pedagogical flow:
- 05_networks → 05_dense (multi-layer dense/fully connected networks)
- 06_cnn → 06_spatial (convolutional networks for spatial patterns)
- 06_attention → 07_attention (attention mechanisms for sequences)
✅ Shifted remaining modules down by 1:
- 07_dataloader → 08_dataloader
- 08_autograd → 09_autograd
- 09_optimizers → 10_optimizers
- 10_training → 11_training
- 11_compression → 12_compression
- 12_kernels → 13_kernels
- 13_benchmarking → 14_benchmarking
- 14_mlops → 15_mlops
- 15_capstone → 16_capstone
✅ Updated module metadata (module.yaml files):
- Updated names, descriptions, dependencies
- Fixed prerequisite chains and enables relationships
- Updated export paths to match new names
New learner progression:
Foundation → Individual Layers → Dense Networks → Spatial Networks → Attention Networks → Training Pipeline
Perfect pedagogical flow: Build one layer → Stack dense layers → Add spatial patterns → Add attention mechanisms → Learn to train them all.
|
2025-07-18 00:12:50 -04:00 |
|