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59d58718f9
✅ 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.
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" |