mirror of
https://github.com/MLSysBook/TinyTorch.git
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5a08d9cfd3
Major Accomplishments: • Rebuilt all 20 modules with comprehensive explanations before each function • Fixed explanatory placement: detailed explanations before implementations, brief descriptions before tests • Enhanced all modules with ASCII diagrams for visual learning • Comprehensive individual module testing and validation • Created milestone directory structure with working examples • Fixed critical Module 01 indentation error (methods were outside Tensor class) Module Status: ✅ Modules 01-07: Fully working (Tensor → Training pipeline) ✅ Milestone 1: Perceptron - ACHIEVED (95% accuracy on 2D data) ✅ Milestone 2: MLP - ACHIEVED (complete training with autograd) ⚠️ Modules 08-20: Mixed results (import dependencies need fixes) Educational Impact: • Students can now learn complete ML pipeline from tensors to training • Clear progression: basic operations → neural networks → optimization • Explanatory sections provide proper context before implementation • Working milestones demonstrate practical ML capabilities Next Steps: • Fix import dependencies in advanced modules (9, 11, 12, 17-20) • Debug timeout issues in modules 14, 15 • First 7 modules provide solid foundation for immediate educational use(https://claude.ai/code)
29 lines
840 B
YAML
29 lines
840 B
YAML
description: 'Precision optimization through INT8 quantization. Students learn to
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reduce model size
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and accelerate inference by using lower precision arithmetic while maintaining accuracy.
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Especially powerful for CNN convolutions and edge deployment.
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'
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difficulty: advanced
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estimated_hours: 6-8
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exports:
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- tinytorch.quantization
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learning_objectives:
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- Understand precision vs performance trade-offs
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- Implement INT8 quantization for neural networks
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- Build calibration-based quantization systems
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- Optimize CNN inference for mobile deployment
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name: Quantization
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number: 17
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prerequisites:
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- Module 09: Spatial (CNNs)
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- Module 16: Acceleration
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skills_developed:
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- Quantization techniques and mathematics
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- Post-training optimization strategies
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- Hardware-aware optimization
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- Mobile and edge deployment patterns
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type: optimization
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