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Level 1 · Entry Foundations

Goal: understand what edge AI is and master the complete TinyML workflow — train a small model with Keras, convert it to TensorFlow Lite (LiteRT), quantize it to int8, turn it into a C array for TFLite-Micro, and understand exactly what changes when it runs on a microcontroller — finishing with a complete gesture-recognition pipeline.

You do not need to own any hardware for this level. Every module runs on a normal laptop with Python and TensorFlow: training, conversion, quantization, and even the C++ inference code (which compiles as a desktop program before it ever touches a board). Microcontroller deployment is covered as the follow-on step in each module, with Wokwi simulator notes where the ESP32 side can be tried in the browser.

Modules

  1. What Is Edge AI
  2. The Edge AI Workflow
  3. Training a Tiny Model
  4. Converting to TFLite/LiteRT
  5. Quantization
  6. TFLite-Micro & C Arrays
  7. Deploying to a Microcontroller
  8. Sensors & Feature Extraction
  9. Evaluating & Debugging Edge Models
  10. Capstone — Gesture Recognition

By the end of this level you'll be able to take a machine-learning idea from a Keras training script to a quantized .tflite flatbuffer to a C array compiled into microcontroller-style firmware — verifying at every step that the model still produces the right answers — and you'll know precisely what RAM, flash, latency, and accuracy each step costs.