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c963c8b676
Clean, dependency-driven organization: - Part I (1-5): MLPs for XORNet - Part II (6-10): CNNs for CIFAR-10 - Part III (11-15): Transformers for TinyGPT Key improvements: - Dropped modules 16-17 (regularization/systems) to maintain scope - Moved normalization to module 13 (Part III where it's needed) - Created three CIFAR-10 examples: random, MLP, CNN - Each part introduces ONE major innovation (FC → Conv → Attention) CIFAR-10 now showcases progression: - test_random_baseline.py: ~10% (random chance) - train_mlp.py: ~55% (no convolutions) - train_cnn.py: ~60%+ (WITH Conv2D - shows why convolutions matter!) This follows actual ML history and each module is needed for its capstone.
31 lines
806 B
YAML
31 lines
806 B
YAML
# TinyTorch Module Metadata
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# Essential system information for CLI tools and build systems
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name: "optimizers"
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title: "Optimizers"
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description: "Gradient-based parameter optimization algorithms"
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# Dependencies - Used by CLI for module ordering and prerequisites
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dependencies:
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prerequisites: ["setup", "tensor", "autograd"]
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enables: ["training", "compression", "mlops"]
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# Package Export - What gets built into tinytorch package
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exports_to: "tinytorch.core.optimizers"
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# File Structure - What files exist in this module
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files:
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dev_file: "optimizers_dev.py"
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readme: "README.md"
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tests: "inline"
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# Educational Metadata
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difficulty: "⭐⭐⭐⭐"
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time_estimate: "6-8 hours"
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# Components - What's implemented in this module
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components:
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- "SGD"
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- "Adam"
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- "StepLR"
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- "gradient_descent_step" |