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Machine Learning at the Edge on Arm: A Practical Introduction

This course will provide you with the hands-on experience you’ll need to create innovative ML applications using ubiquitous Arm-based microcontrollers.

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Machine Learning at the Edge on Arm: A Practical Introduction

This course will provide you with the hands-on experience you’ll need to create innovative ML applications using ubiquitous Arm-based microcontrollers.

6 weeks
3–6 horas por semana
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Hay una sesión disponible:

Una vez finalizada la sesión del curso, será archivadoAbre en una pestaña nueva.
Comienza el 1 sept

Sobre este curso

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The age of machine learning has arrived! Arm technology is powering a new generation of connected devices with sophisticated sensors that can collect a vast range of environmental, spatial and audio/visual data. Typically this data is processed in the cloud using advanced machine learning tools that are enabling new applications reshaping the way we work, travel, live and play.

To improve efficiency and performance, developers are now looking to analyse this data directly on the source device – usually a microcontroller (we call this ‘the Edge[’). But with this approach comes the challenge of implementing machine learning on devices that have constrained computing resources.

This is where our course can help!

By enrolling in Machine Learning at th e Edge on Arm: A Practical Introduction you’ll learn how to train machine learning models and implement them on industry relevant Arm-based microcontrollers.

We’ll start your learning journey by taking you through the basics of AI, ML and ML at the Edge, and illustrate why businesses now need this technology to be available on tiny devices. We’ll then introduce you to the concept of datasets and how to train ML algorithms to recognize patterns, before exploring advanced topics such as Artificial Neural Networks and Computer Vision.

Along the way, our practical lab exercises will show you how you can address real-world design problems in deploying ML applications, such as motion and speech recognition, as well as image processing, using actual sensor data obtained from the microcontroller.

In the final module you’ll be able to apply what you’ve learned by implementing ML algorithms on a dataset of your choice.

The ST DISCO-L475E board used in this course can be purchased directly from our technology partner STMicroelectronics: https://www.st.com/content/st_com/en/campaigns/educationalplatforms/iot-arm-edx-edu.html

Through our vast ecosystem, Arm already powers a wide range of devices and applications that rely on ML at the Edge. Be a part of this vibrant community of developers and start your machine learning journey by enrolling in our course today!

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  • Idioma: English
  • Transcripción de video: English

Lo que aprenderás

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  • An understanding of Artificial Intelligence, Machine Learning and ML concepts.
  • How to get started with machine learning on Arm microcontrollers.
  • How to acquire data from sensors and peripherals on a microcontroller.
  • The fundamentals of Artificial Neural Networks in constrained environments.
  • Convolutional Neural Networks and Deep Learning.
  • How to deploy computer vision models using CMSIS-NN.

Plan de estudios

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Module 1 - Understand basic concepts of AI, ML and Edge ML.

Module 2 - Identify the key features of ML such as datasets, data analysis and ML alogorithm training.

Module 3 - Learn to explain the basic elements of Artificial Neural Networks.

Module 4 - Learn to explain the basic elements of Convolutional Neural Networks (CNN).

Module 5 - Understand how to deploy computer vision using CNN.

Module 6 - Learn to optimise ML models under the constraints of a microcontroller environment

Acerca de los instructores

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

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