Skip to main content

#1 TinyML Aspirants Talk

 Our first talk i.e. a small webinar was held on 20th September 2020 from 4 to 4:45 PM IST over Zoom. Plus the recording was live-streamed from 9 to 9:45 PM IST on that day.

Currently it is available on demand over YouTube through this link -

Do share this to spread the knowledge on this technology


Popular posts from this blog

#2 TinyML Aspirants Talk - Edge Impulse Special

We are overwhelmed and very grateful to have Jan Jongboom - CTO & Co-Founder , Edge Impulse to join us for #2 TinyML Aspirants Talks for a live demo using Edge Impulse. Join us for this live talk on 10th October 2020 from 16:00 to 16:45 IST. PLUS the talk will be live-streamed over YouTube from 21:00 to 21:45 IST on that day.  Zoom Webinar Link - Join the meeting here YouTube Link - (Will be available on that day) Telegram group - Click here to join  Thanks Core Team ,  TinyML Aspirants developer group

Getting Started

Now that you have understood what is TinyML you are probably thinking of testing out some examples and codes. Also you will be thinking that what will be essential required things for that. Well I have got you covered.  Check out this video where Pete Warden explains about getting started with ML on edge devices

Intro to TinyML

 What is TinyML ? Tiny Machine Learning (TinyML) is the intersection of Machine Learning and Embedded IoT Devices. The pervasiveness of ultra-low-power embedded devices, coupled with the introduction of embedded machine learning frameworks like TensorFlow Lite for Microcontrollers, will enable the mass proliferation of AI-powered IoT devices. The explosive growth in machine learning and the ease of use of platforms like TensorFlow (TF) make it an indispensable topic of study for modern computer science and electrical engineering students.  Why is TinyML gaining popularity ? Because of the following features -  Low - in - cost High Security Saves data and time  Is there some kind of explanatory videos ? Sure there is. Check these out from Pete Warden , Technical Lead of Google's Tensorflow Lite for Microcontrollers team