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. Östergötland onsite live TensorFlow Lite trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
TensorFlow Lite is also known as Google's TensorFlow Lite.
NobleProg -- Your Local Training Provider
Linköping
NobleProg classroom in Scandic Linköping, Gamla Tanneforsvägen 51, Linköping, sweden, 582 54
Scandic Linköping City is a modern conference place. It's located in central Linköping and also close to Linkoping Cathedral and Linköping Garden Society. Linköping Airport is just a 5-minute car drive away from the conference centre. Other famous landmarks such as the Swedish Airforce Museum and the Old Town is only a 10-minute drive as well.
This instructor-led, live training in Östergötland (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 Östergötland (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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