In this module, we will look at how neural networks work, how to train them, and how to use them to perform inference in an embedded system. We will continue the previous demo of creating a motion classification system using motion data collected from a smartphone or Arduino board. Finally, we will challenge you with a new motion classification project where you will have the opportunity to implement the concepts learning in this module and the previous module.

Video Contant

Introduction to Neural Networks15m

Model Training in Edge Impulse7m

How to Evaluate a Model10m

Underfitting and Overfitting6m

How to Use a Model for Inference6m

Testing Inference with a Smartphone3m

How to Deploy a Trained Model to Arduino9m

Anomaly Detection8m

Industrial Embedded Machine Learning Demo4m

Module Review3m

10 readings

Neural Networks and Training5m

Slides10m

Evaluation, Underfitting, and Overfitting5m

Slides10m

Using a Model for Inference5m

Slides10m

Anomaly Detection5m

Slides10m

Project - Motion Detection2h

Slides10m

5 practice exercises

Neural Networks and Training15m

Evaluation, Underfitting, and Overfitting15m

Deploy Model to Embedded System15m

Anomaly Detection5m

Motion Classification and Anomaly Detection30m

In this module, we cover audio classification on embedded systems. Specifically, we will go over the basics of extracting mel-frequency cepstral coefficients (MFCCs) as features from recorded audio, training a convolutional neural network (CNN) and deploying that neural network to a microcontroller. Additionally, we dive into some of the implementation strategies on embedded systems and talk about how machine learning compares to sensor fusion.

9 videos

Introduction to Audio Classification7m

Audio Data Capture11m

Audio Feature Extraction10m

Introduction to Convolutional Neural Networks11m

Modifying the Neural Network10m

Deploy Keyword Spotting System6m

Implementation Strategies10m

Sensor Fusion3m

Conclusion2m

7 readings

Sample Rate and Bit Depth5m

Slides10m

MFCCs and CNNs5m

Slides10m

Implementation Strategies and Sensor Fusion5m

Slides10m

Project - Sound Classification2h

4 practice exercises

Audio Classification and Sampling Audio Signals10m

MFCCs and CNNs15m

Implementation Strategies15m

Audio Classification30m