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. Porto Alegre 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
Porto Alegre - Platinum Building
Carlos Gomes Avenue, 700, Porto Alegre, Brazil, 90480-000
Platinum Building has many amenities close by. The Boa Vista district is a major corporate center in this sophisticated and progressive port city. Also the business center is six kilometers from the international airport and well served by public transport.
Porto Alegre - Guaiba Business Center
Av. Dolorez Alcaraz Caldas, 90 Porto Alegre RS, Praia de Belas, Brasil, 90110-180
Be at the centre of a lively business district in Praia De Belas, surrounded by courthouses and government offices. Centro Empresarial Guaiba is a distinctive Class A building in the thriving state capital of Porto Alegre.
This instructor-led, live training in Porto Alegre (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 Porto Alegre (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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