04cbc65724
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 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
7.3 KiB
Quick Start Guide
From Zero to Building Neural Networks
Complete setup + first module in 15 minutes
Purpose: Get hands-on experience building ML systems in 15 minutes. Complete setup verification and build your first neural network component from scratch.
⚡ 2-Minute Setup Verification
Let's make sure you're ready to build ML systems:
Step 1: Install & Verify
# Clone and install
git clone https://github.com/veekaybee/tinytorch.git
cd tinytorch
pip install -e .
Expected output: A working TinyTorch development environment ready for hands-on building.
📖 See Essential Commands for complete setup verification and troubleshooting.
Step 2: Verify Your Starting Point
Confirm you're ready to begin building ML systems from scratch. Your development environment should be configured and ready for hands-on implementation.
📖 See Essential Commands for verification commands and troubleshooting.
🏗️ 15-Minute First Module Walkthrough
Let's build your first neural network component and unlock your first capability:
Module 01: Tensor Foundations
🎯 Learning Goal: Build N-dimensional arrays - the foundation of all neural networks
⏱️ Time: 15 minutes
💻 Action: Start with Module 01 to build tensor operations from scratch.
# Navigate to the tensor module
cd modules/01_tensor
jupyter lab tensor_dev.py
You'll implement core tensor operations:
- N-dimensional array creation
- Basic mathematical operations (add, multiply, matmul)
- Shape manipulation (reshape, transpose)
- Memory layout understanding
Key Implementation: Build the Tensor class that forms the foundation of all neural networks
📖 See Essential Commands for module workflow commands.
✅ Achievement Unlocked: Foundation capability - "Can I create and manipulate the building blocks of ML?"
Next Step: Module 02 - Activations
🎯 Learning Goal: Add nonlinearity - the key to neural network intelligence
⏱️ Time: 10 minutes
💻 Action: Continue with Module 02 to add activation functions.
You'll implement essential activation functions:
- ReLU (Rectified Linear Unit) - the workhorse of deep learning
- Softmax - for probability distributions
- Understand gradient flow and numerical stability
- Learn why nonlinearity enables learning
Key Implementation: Build activation functions that allow neural networks to learn complex patterns
📖 See Essential Commands for module development workflow.
✅ Achievement Unlocked: Intelligence capability - "Can I add nonlinearity to enable learning?"
📊 Track Your Progress
After completing your first modules:
Check your new capabilities: Track your progress through the 21-checkpoint system to see your growing ML systems expertise.
📖 See Track Your Progress for detailed capability tracking and Essential Commands** for progress monitoring commands.
🎯 What You Just Accomplished
In 15 minutes, you've:
🔧 Setup Complete
Installed TinyTorch and verified your environment
🧱 Created Foundation
Implemented core tensor operations from scratch
🏆 First Capability
Earned your first ML systems capability checkpoint
🚀 Your Next Steps
Immediate Next Actions (Choose One):
🔥 Continue Building (Recommended): Begin Module 03 to add intelligence to your network with nonlinear activation functions.
📚 Learn the Workflow:
- 📖 See Essential Commands for complete TITO command guide
- 📖 See Track Your Progress for the full learning path
🎓 For Instructors:
- 📖 See Classroom Setup Guide for NBGrader integration and grading workflow
💡 Pro Tips for Continued Success
Essential Development Practices:
- Always verify your environment before starting
- Track your progress through capability checkpoints
- Follow the standard module development workflow
- Use diagnostic commands when debugging issues
📖 See Essential Commands for complete workflow commands and troubleshooting guide.
🌟 You're Now a TinyTorch Builder!
Ready to Build Production ML Systems
You've proven you can build ML components from scratch. Time to keep going!
Continue Building → Master Commands →What makes TinyTorch different: You're not just learning about neural networks—you're building them from fundamental mathematical operations. Every line of code you write builds toward complete ML systems mastery.
Next milestone: After Module 08, you'll train real neural networks on actual datasets using 100% your own code!