Md Shahriyar Al Mustakim Mitulmitul-shahriyar.hashnode.dev·May 25, 2024TinyML (Part 8): Avoiding OverfittingBecause ML can be power hungry, we want to make sure we're as efficient as possible in classifying. And misclassifications, due to overfitting, might really hurt your app. Imagine if the only shoes you had ever seen in your life were hiking boots. N...Discuss#TINYML
Md Shahriyar Al Mustakim Mitulmitul-shahriyar.hashnode.dev·May 25, 2024TinyML (Part 7)One useful data set for learning is the Horses or Humans dataOne useful data set for learning is the Horses or Humans datac set, which has over 1,000 images that are 300 by 300 in full color of horses and humans in various poses. Note that unlike MNI...Discuss#TINYML
Md Shahriyar Al Mustakim Mitulmitul-shahriyar.hashnode.dev·May 25, 2024TinyML (Part 2)Minimizing loss with Gradiant descent Assume this is our loss function If we want to know the minimum of the loss function, we just look to the bottom of the parabola. It doesn't matter whatever the parameters that make up the function are and where...Discuss#TINYML
Md Shahriyar Al Mustakim Mitulmitul-shahriyar.hashnode.dev·May 23, 2024TinyML (Part 1)Let's learn it by its application: You may have said "Hey siri" to your apple devices Some applications of Tinyml nowadays: But imagine smart glasses in the future. Imagine a situation where you go out into the mall, or marketplace, or you're at a ...Discuss#TINYML
neuailabsneuailabs01.hashnode.dev·Dec 7, 2023Revolutionizing Automotive Embedded Systems with TinyML: A Roadmap to Efficiency and InnovationTechnology breakthroughs are changing the driving experience, and this is transforming the car business. TinyML, a ground-breaking technology that pushes machine learning capabilities to the limit of embedded systems, is one of the main forces behind...Discussautomotive embedded system
Mithilesh GaikwadforMithilesh's Blogesymith.hashnode.dev·Dec 4, 2023TinyML for Advanced Embedded SystemsTiny Machine Learning (TinyML) has emerged as a transformative paradigm, enabling the integration of machine learning models into resource-constrained embedded systems. These systems, prevalent in Internet of Things (IoT) devices, wearables, and edge...Discuss·10 likes#TINYML
Ryan kiprotich cheruiyotforTinyMl:Ryanryantalkstinyml.hashnode.dev·Aug 18, 2023Deploying Tensorflow Model on a MicrocontrollerIntroduction Image by Vishnu Mohanan from Unsplash Deploying machine learning and deep learning models offers numerous methods. One of the most popular approaches is Server-Based Deployment, achieved through Web APIs or cloud-based servic...Discuss·9 likes·558 readsTensorFlow
Shankar Rshankaarrr.hashnode.dev·Jun 22, 2023Getting Started with TinyML: Unlocking the Potential of Smart Embedded DevicesWelcome to Shankar's POV I embark on an exciting blogging adventure into the world of Technology. I'm thrilled to have you join me on this journey as we explore the possibilities and potential of TinyML. This blog is a space for me to share my though...Discuss·3 likes·134 readsWeMakeDevs
Tinyml kenyatinymlkenya.hashnode.dev·Feb 20, 2023A Guide to Getting Started with Arduino Nano 33 BLE SenseIf you are thinking about deploying a machine learning or an Artificial intelligence project on the Edge, the Arduino Nano 33 BLE Sense is a great device to start with.The Arduino Nano 33 BLE Sense combines a tiny form factor, different environment ...Discuss·13 likes·389 readsarduino
Tinyml kenyatinymlkenya.hashnode.dev·Dec 22, 2022A guide to performing Keyword Spotting on Microcontrollers using Edge ImpulseThis tutorial will go through the steps you need to build a Keyword spotting application. In our example, we will build an application that can detect when a person is in distress and needs help by detecting the Keyword Help Keyword spotting is the...Discuss·3 likes·316 reads#TINYML
