06b35c34bd
Major fixes for complete training pipeline functionality: Core Components Fixed: - Parameter class: Now wraps Variables with requires_grad=True for proper gradient tracking - Variable.sum(): Essential for scalar loss computation from multi-element tensors - Gradient handling: Fixed memoryview issues in autograd and activations - Tensor indexing: Added __getitem__ support for weight inspection Training Results: - XOR learning: 100% accuracy (4/4) - network successfully learns XOR function - Linear regression: Weight=1.991 (target=2.0), Bias=0.980 (target=1.0) - Integration tests: 21/22 passing (95.5% success rate) - Module tests: All individual modules passing - General functionality: 4/5 tests passing with core training working Technical Details: - Fixed gradient data access patterns throughout activations.py - Added safe memoryview handling in Variable.backward() - Implemented proper Parameter-Variable delegation - Added Tensor subscripting for debugging access(https://claude.ai/code)
6.3 KiB
Track Your Progress
Monitor Your Learning Journey
Track your capability development through 16 essential ML systems skills
Purpose: Monitor your capability development through the 21-checkpoint system. Track progress from foundation skills to production ML systems mastery.
Track your progression through 21 essential ML systems capabilities. Each checkpoint represents fundamental competencies you'll master through hands-on implementation—from tensor operations to production-ready systems.
How to Track Your Progress
🎯 Capability-Based Learning
Use TinyTorch's 21-checkpoint system to monitor your capability development. Track progress from foundation skills to production ML systems mastery.
📖 See Essential Commands for complete progress tracking commands and workflow.
Your Learning Path Overview
TinyTorch organizes learning through four major phases, each building essential ML systems capabilities:
📖 See Complete Course Structure for the full learning timeline and detailed module descriptions.
Student Learning Journey
Typical Student Progression
- Week 1-2: Foundation capabilities (Environment, Tensors, Activations)
- Week 3-4: Core learning systems (Layers, Losses, Autograd)
- Week 5-6: Training and optimization (Optimizers, Training loops)
- Week 7-8: Advanced architectures (Spatial processing, Attention)
- Week 9-12: Production systems (Profiling, Optimization, Deployment)
Study Approaches
- Full Implementation (8-12 weeks): Build every component from scratch
- Guided Study (4-6 weeks): Study solution notebooks with implementation exercises
- Quick Exploration (2 weeks): Focus on key concepts with provided implementations
📖 See Quick Start Guide for immediate hands-on experience with your first module.
21 Core Capabilities
Track progress through essential ML systems competencies:
:class: note
Each checkpoint validates mastery of fundamental ML systems skills.
| Checkpoint | Capability Question | Modules Required | Status |
|---|---|---|---|
| 00 | Can I set up my environment? | 01 | ⬜ Setup |
| 01 | Can I manipulate tensors? | 02 | ⬜ Foundation |
| 02 | Can I add nonlinearity? | 03 | ⬜ Intelligence |
| 03 | Can I build network layers? | 04 | ⬜ Components |
| 04 | Can I measure loss? | 05 | ⬜ Networks |
| 05 | Can I compute gradients? | 06 | ⬜ Learning |
| 06 | Can I optimize parameters? | 07 | ⬜ Optimization |
| 07 | Can I train models? | 08 | ⬜ Training |
| 08 | Can I process images? | 09 | ⬜ Vision |
| 09 | Can I load data efficiently? | 10 | ⬜ Data |
| 10 | Can I process text? | 11 | ⬜ Language |
| 11 | Can I create embeddings? | 12 | ⬜ Representation |
| 12 | Can I implement attention? | 13 | ⬜ Attention |
| 13 | Can I build transformers? | 14 | ⬜ Architecture |
| 14 | Can I profile performance? | 14 | ⬜ Deployment |
| 15 | Can I accelerate algorithms? | 15 | ⬜ Acceleration |
| 16 | Can I quantize models? | 16 | ⬜ Quantization |
| 17 | Can I compress networks? | 17 | ⬜ Compression |
| 18 | Can I cache computations? | 18 | ⬜ Caching |
| 19 | Can I benchmark competitively? | 19 | ⬜ Competition |
| 20 | Can I build complete language models? | 20 | ⬜ TinyGPT Capstone |
📖 See Essential Commands for progress monitoring commands.
Capability Development Approach
Foundation Building (Checkpoints 0-3)
Capability Focus: Core computational infrastructure
- Environment configuration and dependency management
- Mathematical foundations with tensor operations
- Neural intelligence through nonlinear activation functions
- Network component abstractions and forward propagation
Learning Systems (Checkpoints 4-7)
Capability Focus: Training and optimization
- Loss measurement and error quantification
- Automatic differentiation for gradient computation
- Parameter optimization with advanced algorithms
- Complete training loop implementation
Advanced Architectures (Checkpoints 8-13)
Capability Focus: Specialized neural networks
- Spatial processing for computer vision systems
- Efficient data loading and preprocessing pipelines
- Natural language processing and tokenization
- Representation learning with embeddings
- Attention mechanisms for sequence understanding
- Complete transformer architecture mastery
Production Systems (Checkpoints 14-15)
Capability Focus: Performance and deployment
- Profiling, optimization, and bottleneck analysis
- End-to-end ML systems engineering
- Production-ready deployment and monitoring
Start Building Capabilities
Begin developing ML systems competencies immediately:
Begin Capability Development
Start with foundational capabilities and progress systematically
15-Minute Start → Begin Setup →Track Your Progress
To monitor your capability development and learning progression, use the TITO checkpoint commands.
📖 See Essential Commands for complete command reference and usage examples.
Approach: You're building ML systems engineering capabilities through hands-on implementation. Each capability checkpoint validates practical competency, not just theoretical understanding.