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- Add Module Info sections with difficulty ratings to all README.md files - Use consistent 4-star difficulty scale: ⭐ Beginner, ⭐⭐ Intermediate, ⭐⭐⭐ Advanced, ⭐⭐⭐⭐ Expert - Include time estimates, prerequisites, and next steps for each module - Maintain clear separation: README.md = student experience, module.yaml = system metadata - Difficulty progression: Setup (⭐) → Tensor/Activations/Layers (⭐⭐) → Networks/CNN/DataLoader (⭐⭐⭐) → Transformer (⭐⭐⭐⭐) - Help students plan their learning journey and set appropriate expectations
1.6 KiB
1.6 KiB
Transformer Module
📊 Module Info
- Difficulty: ⭐⭐⭐⭐ Expert
- Time Estimate: 8-12 hours
- Prerequisites: Tensor, Layers, Networks, Autograd, Training modules
- Next Steps: Advanced NLP, Large Language Models
Status: 🚧 Coming Soon
Overview
The Transformer module will be a lightweight implementation of transformer architecture, teaching students how modern attention-based models work from the ground up.
Learning Goals
- Understand attention mechanisms and their computational complexity
- Implement multi-head attention from scratch
- Learn about positional encoding and layer normalization
- Explore transformer architecture design patterns
- Understand memory and computational optimization for attention
Module Dependencies
This module builds on:
- tensor - For all computations
- layers - For feed-forward networks
- networks - For composing transformer blocks
- autograd - For training attention models
- training - For training transformer models
Planned Components
- Attention mechanism implementation
- Multi-head attention
- Positional encoding
- Layer normalization
- Transformer blocks
- Complete transformer architecture
- Memory optimization techniques
- Attention visualization tools
Systems Focus
- Memory management for attention matrices
- Computational complexity analysis
- Parallelization of multi-head attention
- Optimization techniques (sparse attention, linear attention)
- Scaling considerations for large sequences
This module will be implemented after the core modules (tensor, layers, networks, autograd, training) are complete.