Machine Learning at the Edge on Arm: A Practical Introduction

via edX

Overview

Embark on a transformative learning journey with Machine Learning at the Edge on Arm: A Practical Introduction, hosted by edX. This course is designed to illuminate the impactful ways Arm technology enables connected devices to process vast arrays of data locally on microcontrollers—commonly referred to as 'the Edge'—and how this strategy enhances efficiency and performance over traditional cloud-based processing.

Participants will gain hands-on experience in training machine learning models and implementing them on industry-leading Arm-based microcontrollers. The course begins by covering the fundamentals of artificial intelligence, machine learning, and machine learning at the edge, highlighting their necessity in modern business applications on connected devices.

Dive into practical aspects by learning about datasets, and algorithm training using popular tools like Anaconda and Python. Explore advanced topics including artificial neural networks and computer vision. Engage in practical lab exercises that tackle real-world design challenges in deploying machine applications for speech and pattern recognition, as well as image processing using actual sensor data.

The program also introduces participants to TensorFlow Python library, a pivotal tool for training and inferring deep neural networks. Through these comprehensive modules, learners will eventually apply their knowledge to implement machine learning algorithms on a selected dataset.

The core hardware used in this course is the ST DISCO-L475E board, available for purchase through our technology partner STMicroelectronics at STMicroelectronics.

Join a global community of developers leveraging machine learning at the edge by enrolling in this course today. Navigate the cutting-edge landscape of machine learning technologies and discover how they're crafting the future of how we interact with the world around us.

Categories include: Artificial Intelligence Courses, Machine Sound Courses, Computer Vision Courses, and Microcontrollers Courses.