Explore the world of TinyML, where machine learning meets resource-constrained devices. This category covers the theory, tools, and practical implementations of running intelligent algorithms on microcontrollers and low-power hardware. Discover tutorials, research insights, and real-world applications of TinyML in wearable devices, IoT sensors, industrial automation, and autonomous drones. Whether you’re an embedded developer, data scientist, or AI enthusiast, this category helps you understand how to bring smart capabilities to devices with limited memory, processing power, and energy.
Foundations
- What is TinyML and Why it Matters for Resource-Constrained Devices
- Understanding TinyML Inference on Resource-Constrained Devices
- TinyML Software Stacks Overview: Tools for Running AI on Microcontrollers
- Mastering the Training Phase in TinyML: Foundations for Embedded AI
Project Ideas
- TinyML Projects with the ESP32 WROVER Kit and Common Arduino Sensors
- TinyML vs. Dedicated Voice Recognition Modules for Embedded Projects
- Building a Voice-Controlled Toy Car with TinyML
- Introduction to a Voice-Controlled RGB LED with TinyML on ESP32
- Building a Smart Motion Detector with TinyML and ESP32 WROVER
- Gesture Recognition with an Accelerometer using TinyML and ESP32 WROVER Kit
- Environmental Anomaly Detection with Microcontrollers and TinyML