TinyML with Arduino Nano RP2040 Connect
Published 10/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 16 lectures (1h 35m) | Size: 669 MB
Machine learning model development for tiny low power microcontroller such as Arduino nano RP2040 connect
What you'll learn
To be able to understand hardware requirement for development of machine learning model for tiny MCUs
Understanding the tinyML development framework
To be able to create tinyML projects based upon hand gesture
To be able to develop tinyML model with audio keyword detection
Requirements
Arduino nano-RP2040 connect board, USB cable, PC/Laptop, Basic knowledge about Arduino IDE, basic knowledge of machine learning, basics of embedded C/C++
Description
**Note: This course is not finalized yet. As you know, the TinyML field is constantly growing and developing. So, keeping in mind more sections with theoretical explanations with hands-on project ideas will be included in the near future.
Tiny machine learning, which targets battery-operated devices, is broadly defined as a rapidly expanding field of machine learning technologies and applications that includes hardware (dedicated integrated circuits), algorithms, and software that can perform on-device sensor data analytics at extremely low power, typically in the mW range and below. It eliminates the requirement to send data to the cloud for classification thus providing more security. Also, power-hungry processors are being replaced by a tiny MCU. Of course, there are limitations. The limitations came from limited hardware resources, clock speed, etc. Still, there are several application areas where high computation is not required and a machine learning-based solution is desirable. In that case, TinyML will come into the picture. It can be used to detect anomalies...
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