TinyML - Using Machine Learning on Microcontrollers to Recognize Speech

Explore the world of TinyML in this comprehensive workshop on implementing machine learning on microcontrollers for speech recognition. Learn to train a neural network to recognize spoken words, convert it to a TensorFlow Lite model, and deploy it on an ARM microcontroller for real-time wake word detection. Dive into embedded machine learning concepts, data collection techniques, and the intricacies of creating a speech recognition project. Gain insights into AI, machine learning, and deep learning, while following along with hands-on exercises using Edge Impulse. Discover the potential of running neural networks on microcontrollers and their practical applications in this engaging session led by electrical and embedded engineer Shawn Hymel.

Introduction
Welcome
Project Overview
What is Embedded Machine Learning
Keyword Spotting Worksheet
Data Collection
Creating Speech Recognition Project
Python Script
Data Augmentation
AI vs ML
What is deep learning
AI ML and deep learning
Questions
Technology Pipeline
Edge Impulse
Test Data First