v0.1.0: Foundational TinyML Systems
Actual Release Date: December 12, 2023
This initial release establishes the foundations of Tiny Machine Learning (TinyML) systems education, presenting a comprehensive introduction to resource-constrained AI deployment and embedded machine learning.
🎯 Key Highlights
📖 Core Content (45 files)
- Complete TinyML Curriculum: 15+ foundational chapters covering embedded AI systems
- Deep Learning Primer: Neural network fundamentals optimized for embedded systems
- Hardware Acceleration: Coverage of ASICs, FPGAs, and microcontroller deployments
- Model Optimization: Quantization, pruning, and compression for resource constraints
- Benchmarking Framework: MLPerf and performance evaluation methodologies
🛠️ Technical Infrastructure
- Quarto-based Publishing: Modern, reproducible academic publishing framework
- Multi-format Output: Synchronized HTML, PDF, and EPUB generation
- Reference Management: Comprehensive bibliography system
- Interactive Labs: Practical examples with Arduino, ESP32, and Raspberry Pi
🎓 Educational Foundation
- University-level Curriculum: Suitable for CS and engineering programs
- Hands-on Learning: Real hardware deployment on embedded platforms
- Open Source: Fully accessible content and collaborative development
- Academic Rigor: Peer-reviewed content with extensive citations
📊 Content Overview
- Total Files: 45 QMD source files
- Core Chapters: 15+ comprehensive chapters
- Hardware Platforms: Arduino Nicla Vision, ESP32, Raspberry Pi
- Lab Exercises: Multiple hands-on deployment tutorials
📋 Release Information
- Release Date: December 12, 2023
- Development Period: 2023
- Content Focus: TinyML fundamentals and embedded AI
- Target Audience: Students, educators, embedded systems engineers
🔗 Quick Links
🏗️ Technical Details
- Build Platform: Quarto with R and Python
- Formats: HTML, PDF, EPUB
- License: Open source educational content
- PDF Engine: LaTeX with custom templates
This foundational release established the first comprehensive academic textbook dedicated to TinyML systems, empowering students and practitioners to master embedded ML from theory to deployment on resource-constrained devices.