Track Your Progress

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:

Capability Tracking

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 →