Online or onsite, instructor-led live TensorFlow Lite training courses demonstrate through interactive hands-on practice how to use TensorFlow Lite to to deploy deep learning models on embedded devices.
TensorFlow Lite training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live TensorFlow Lite training can be carried out locally on customer premises in Innsbruck or in NobleProg corporate training centers in Innsbruck.
TensorFlow Lite is also known as Google's TensorFlow Lite.
NobleProg -- Your Local Training Provider
NobleProg Innsbruck
Valiergasse 58, Innsbruck, Austria, 6020
Overview
Our training facilities are located at Valiergasse 58 in Innsbruck and offer optimal training conditions for your needs.
Directions
The NobleProg training facilities are conveniently located near the main train station, the A12 and A13 motorways are easily accessible.
Parking spaces
There are parking spaces in the surrounding streets around our training rooms.
Local infrastructure
There are numerous restaurants in the downtown area and hotels are also within walking distance.
This instructor-led, live training in Innsbruck (online or onsite) is aimed at intermediate-level developers, data scientists, and AI practitioners who wish to leverage TensorFlow Lite for Edge AI applications.
By the end of this training, participants will be able to:
Understand the fundamentals of TensorFlow Lite and its role in Edge AI.
Develop and optimize AI models using TensorFlow Lite.
Deploy TensorFlow Lite models on various edge devices.
Utilize tools and techniques for model conversion and optimization.
Implement practical Edge AI applications using TensorFlow Lite.
This instructor-led, live training in Innsbruck (online or onsite) is aimed at engineers who wish to write, load and run machine learning models on very small embedded devices.
By the end of this training, participants will be able to:
Install TensorFlow Lite.
Load machine learning models onto an embedded device to enable it to detect speech, classify images, etc.
Add AI to hardware devices without relying on network connectivity.
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