TinyML with Arduino Nano RP2040 Connect

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Subir Maity

1:35:19

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1. Introduction

1. Introduction.mp4

04:04

2. Getting Started with Nano RP2040 connect board

1. Overview and Specification.mp4

05:08

2. Setting up Arduino IDE and testing the board.mp4

02:03

3. Testing on-board Accelerometer and Gyroscope.mp4

02:27

4. Testing on-board Microphone.mp4

01:54

5.1 nano_rp2040_tricolor_led.zip

5. Testing on-board Tri-color LED.mp4

01:51

3. TinyML Development Framework

1. Setting up development framework.mp4

03:34

2.1 dataset_circle_wave.zip

2. Loading dataset.mp4

05:56

3. Configuring processing blocks.mp4

03:13

4. Hand Gesture Recognition Wavy and Circular Gesture

1.1 dataset_circle_wave.zip

1. Dataset preparation.mp4

03:56

2. Pre-processing and training.mp4

10:03

3.1 ei-nano_rp2040_gesture01-arduino-1.0.1.zip

3.2 nano_rp2040_circle_wave.zip

3. Model deployment and hardware testing.mp4

10:54

5. Audio Recognition Voice Controlled RGB LED

1. Audio sample collection.mp4

07:54

2. Preprocessing and Model training.mp4

08:14

3.1 ei-nano_rp2040_pdm-arduino-1.0.1.zip

3.2 microphone_rgb.zip

3. Model deployment and Hardware testing.mp4

05:19

6. Additional contents

1.1 Cat dog audio Dataset.html

1.2 cat_dog_sound.zip

1.3 ei-cat_dog_sound-arduino-1.0.1.zip

1. Cat-Dog classification based on meow or barking sound.mp4

18:49

Description

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

Who is this for?

Beginner, interested to develop machine learning model in low cost, low power microcontroller

More details

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 in machinery in a factory, it can predict maintenance requirements of the instruments, healthcare field, and so on. The application domain of TinyML is wide and the future is bright.
The primary objective of this course is to be familiar with TinyML development starting from data collection, model training, testing, and deployment. A low-cost Arduino nano RP2040 connect board having 265KB RAM and 16MB flash with in built accelerometer, Gyroscope, Microphone, temperature sensor, and wireless connectivity module (WiFi+Bluetooth) is used in this course and all example demonstrated here is tested on this board.
Who this course is for:
Beginner, interested to develop machine learning model in low cost, low power microcontroller

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Arduino

Subir Maity

Instructor's Courses

Having more than 10 years of experience in teaching and research. Subject taught: Analog Electronics, VLSI circuits, and systems, MOS device Modeling. Research interest: semiconductor devices, MOSFET with III-V channel material, Quantum well MOSFET and High electron mobility transistor, TCAD-based modeling, and simulation, Compact model development of Nano-scale MOSFET, Negative capacitance FETs, MOSFET-based bio-sensors, Verilog-A based model development, Neural network-based model development.

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