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It is even more difficult deploying an ML Model on a resource constraint devices.

Learn more on how to run ML on Arduino Nano 33: https://youtu.be/Fy_dqeNu2IA

You can as well run your Image ML Model on the Arduino Nano 33 BLE Sense

8 Object Classification using the ARduino Nano 33 BLE | TinyML | CoLab | Python | TensorFlow Lite PLEASE LIKE AND SUBSCRIBE.Hello there and welcome to this tutorial. I will be walking you through how to build an object recognition model on the Arduino Nan...

Still on the TinyML Series

7 Building Weather Station Using Tensorflow Lite for Microcontrollers | Python Colab Arduino PLEEEEAAAASE SUBSCRIBE TO MY CHANNEL, IT MEANS A LOT TO MEWe continue in this tutorial by looking at how to build a Tensorflow Lite model from scratch withou...

One of the most important board where you can deploy your TinyML workloads is the Arduino Nano 33 BLE sense

TinyML: What is Arduino Nano 33 BLE Sense? #viralreelschallenge #reelsvideoシ #AI #viralvideo

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If you are looking at starting out in Data Science, check this out for a comprehensive understanding of all the underlying statistics.

Data Science Statistics for Absolute Beginners Beginners approach to technicalities of Statistics for Data Science

Tiny Machine Learning involves running machine learning algorithms on low-power microcontrollers or other resource-constrained devices. Enables real-time, low-latency inference on edge devices without relying on cloud connectivity. TinyML can lead to advancements in various fields, including healthcare, agriculture, smart homes, and more.

The advantages of TinyML were summarised by Jeff Bier with the acronym BLERP

I started creating some beginner tutorials on TinyML, you can check it out using this Link: https://youtube.com/playlist?list=PL6vSC9suLAKM2kIRF8O65K5SgQdvzHM69&si=TWaGQnZpDVCN-H-v

In TinyML, where resources are limited, model size plays a critical role. Traditional machine learning models can be quite large, requiring significant storage space and computational power. This is where quantization comes in.
The Problem: Large Models, Tiny Devices
TinyML applications run on microcontrollers and embedded devices with limited memory (RAM) and processing power (CPU). Large models typically:
• Occupy too much memory: They might not fit on the device entirely or leave insufficient space for other tasks.
• Demand high processing power: Running complex calculations on these models can drain the battery quickly and slow down the device.
The Solution: Quantization - Shrinking the Model for Efficiency
Quantization is a technique used to reduce the size of a machine learning model by reducing the precision of its weights and activations

https://youtu.be/9sJ3RzJPTbs?si=FaOkh1cWufe2qF4b

1 TinyML Introduction and Overview Forget the cloud! Discover TinyML, the incredible way to put machine learning directly onto tiny devices. What can you build with this? Find out in this intr...

Forget the cloud! Discover TinyML, the incredible way to put machine learning directly onto tiny devices. What can you build with this? Find out in this intro to TinyML and get ready to be amazed.

1 TinyML Introduction and Overview Forget the cloud! Discover TinyML, the incredible way to put machine learning directly onto tiny devices. What can you build with this? Find out in this intr...