mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-05-10 16:38:39 -05:00
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)
24 lines
435 B
YAML
24 lines
435 B
YAML
components:
|
|
- SGD
|
|
- Adam
|
|
- StepLR
|
|
- gradient_descent_step
|
|
dependencies:
|
|
enables:
|
|
- training
|
|
- compression
|
|
- mlops
|
|
prerequisites:
|
|
- tensor
|
|
- autograd
|
|
description: Gradient-based parameter optimization algorithms
|
|
difficulty: "\u2B50\u2B50\u2B50\u2B50"
|
|
exports_to: tinytorch.core.optimizers
|
|
files:
|
|
dev_file: optimizers_dev.py
|
|
readme: README.md
|
|
tests: inline
|
|
name: optimizers
|
|
time_estimate: 6-8 hours
|
|
title: Optimizers
|