Conducted either online or onsite, these instructor-led live TensorFlow Lite training courses feature interactive, hands-on practice that demonstrates how to effectively deploy deep learning models on embedded devices.
TensorFlow Lite training is offered as "online live training" or "onsite live training". Online live training (also referred to as "remote live training") is delivered through an interactive, remote desktop. Onsite live training takes place locally at the customer's premises in Copenhagen or at NobleProg's corporate training centers in Copenhagen.
TensorFlow Lite is also recognized as Google's TensorFlow Lite.
You can also take bus 26 or 7A and get off around Vesterbros Torv; from there it is a short walk.
By bus
Useful nearby stops include:
Vesterbros Torv (Vesterbrogade) — about 1 minute from the area.
Frederiksberg Allé (Vesterbrogade) — nearby.
Trommesalen (Vesterbrogade) — nearby.
Bus 26 and 7A serve the Vesterbrogade corridor.
København, Copenhagen
NobleProg København, Bredgade 37, København K, denmark, 1260
Near Amalienborg Palace & Royal Theatre.
If you arrive by train
If you arrive at Copenhagen Central Station (København H):
Walk to København H Metro.
Take the M3 or M4 metro toward Kongens Nytorv.
Get off at Kongens Nytorv.
Walk north toward Nyhavn, then continue along Bredgade.
Bredgade 37 is about 500 m / 6 minutes' walk from Kongens Nytorv.
Another easy option
Marmorkirken (Marble Church) Metro Station is also very close to Bredgade 37. The nearby Bredgade area is about 2 minutes' walk from Marmorkirken station.
By bus
There are bus stops around Dronningens Tværgade / Bredgade, so you can also travel by bus and walk the final few minutes.
This live, instructor-led training, available in Copenhagen either online or onsite, is tailored for intermediate-level developers, data scientists, and AI practitioners seeking to utilize TensorFlow Lite for building Edge AI applications.
Upon completion of this program, participants will be equipped to:
Comprehend the essential components of TensorFlow Lite and its strategic importance in Edge AI.
Construct and optimize AI models leveraging the TensorFlow Lite framework.
Deploy TensorFlow Lite models onto a variety of edge devices.
Employ specific tools and methodologies for model conversion and optimization.
Develop and implement practical Edge AI solutions using TensorFlow Lite.
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