Available either online or on-site, these instructor-led live TensorFlow Lite courses utilize interactive, hands-on exercises to demonstrate the deployment of deep learning models on embedded devices.
TensorFlow Lite training is offered as either "online live training" or "onsite live training". The online live option, also referred to as "remote live training", is delivered through an interactive remote desktop. Onsite live training can be conducted either locally at the client's premises in Bangkok or at NobleProg's corporate training centers in Bangkok.
TensorFlow Lite is also recognized as Google's TensorFlow Lite.
NobleProg -- Your Local Training Provider
Empire Tower
1, South Sathorn Road,, Bangkok, thailand, 10120
Ways to get there
BTS Skytrain: Take the Silom Line and get off at BTS Chong Nonsi. The Empire is within walking distance; the building has access from the Sathorn/Naradhiwas area.
BTS Saint Louis:Saint Louis station is also nearby, around a 7-minute walk according to current location information.
BRT: Get off at BRT Sathorn. It's approximately a 2–4 minute walk to the building.
MRT:MRT Silom is another option, followed by a walk toward Sathorn.
Taxi/Grab: Give the driver: “The Empire, 1 South Sathorn Road, Sathon, Bangkok 10120.” The official building site confirms this address.
By car: The building can be accessed from Sathorn Road or Naradhiwas Rajanagarinda Road.
This instructor-led live training, offered in Bangkok (either online or onsite), is tailored for intermediate-level developers, data scientists, and AI practitioners seeking to maximize the potential of TensorFlow Lite in Edge AI scenarios.
By the conclusion of this program, participants will be equipped to:
Comprehend the essential mechanics of TensorFlow Lite and its significance in Edge AI.
Build and refine AI models through the use of TensorFlow Lite.
Deploy TensorFlow Lite models onto a variety of edge devices.
Leverage specialized tools and methods for model conversion and efficiency improvements.
Create practical Edge AI implementations using TensorFlow Lite.
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