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✅ Rename all module directories: 00_setup → 01_setup, etc. ✅ Update convert_modules.py mappings for new directory names ✅ Update _toc.yml file paths and titles (1-14 instead of 0-13) ✅ Regenerate all overview pages with new numbering ✅ Fix all broken references in usage-paths and intro ✅ Update chapter references to use natural numbering Benefits: - More intuitive course progression starting from 1 - Matches academic course numbering conventions - Eliminates confusion about 'Module 0' concept - Cleaner mental model for students and instructors - All references and links properly updated Complete transformation: 14 modules now numbered 01-14
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DataLoader - Data Loading and Preprocessing
Welcome to the DataLoader module! This is where you'll learn how to efficiently load, process, and manage data for machine learning systems.
:class: tip
- Understand data pipelines as the foundation of ML systems
- Implement efficient data loading with memory management and batching
- Build reusable dataset abstractions for different data types
- Master the Dataset and DataLoader pattern used in all ML frameworks
- Learn systems thinking for data engineering and I/O optimization
Build → Use → Reflect
- Build: Create dataset classes and data loaders from scratch
- Use: Load real datasets and feed them to neural networks
- Reflect: How data engineering affects system performance and scalability
What You'll Learn
By the end of this module, you'll understand:
- The Dataset pattern for consistent data access
- How DataLoaders enable efficient batch processing
- Why batching and shuffling are crucial for ML
- How to handle datasets larger than memory
- The connection between data engineering and model performance
🚀 Interactive Learning
Choose your preferred way to engage with this module:
```{grid-item-card} 🚀 Launch Binder
:link: https://mybinder.org/v2/gh/mlsysbook/TinyTorch/main?filepath=modules/source/07_dataloader/dataloader_dev.ipynb
:class-header: bg-light
Run this module interactively in your browser. No installation required!
```
```{grid-item-card} ⚡ Open in Colab
:link: https://colab.research.google.com/github/mlsysbook/TinyTorch/blob/main/modules/source/07_dataloader/dataloader_dev.ipynb
:class-header: bg-light
Use Google Colab for GPU access and cloud compute power.
```
```{grid-item-card} 📖 View Source
:link: https://github.com/mlsysbook/TinyTorch/blob/main/modules/source/07_dataloader/dataloader_dev.py
:class-header: bg-light
Browse the Python source code and understand the implementation.
```
:class: tip
**Binder sessions are temporary!** Download your completed notebook when done, or switch to local development for persistent work.
Ready for serious development? → [🏗️ Local Setup Guide](../usage-paths/serious-development.md)