Online or onsite, instructor-led live Apache MXNet training courses demonstrate through interactive hands-on practice how to use Apache MXNet to build and deploy a deep learning model for image recognition.
Apache MXNet 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 Apache MXNet trainings in Brugge can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Bruges
NH Hotel Bruges, Boeveriestraat 2, Bruges, Belgium, 8000
Bruges became a central port, commercial and financial hub in medieval Europe, linking the countries of the North Sea and ...
Bruges became a central port, commercial and financial hub in medieval Europe, linking the countries of the North Sea and the Baltic to the Mediterranean. Wealthy merchants from Bruges traded with those from all over Europe. The first stock exchange in history was born in Bruges in the 13th century. In the 15th century it was the leading financial center in Europe. This economic boom also leads to a cultural and artistic flowering that has left an abundant heritage. It was the most important center for the Flemish primitive painters, who revolutionized Western painting. It has been a member of the Organization of World Heritage Cities since the year 2000. The city even has the distinction of appearing three times on the UNESCO World Heritage List. For its historic center, for its beguinage which is part of the Flemish Beguinages and for its belfry included among the Belfries of Belgium and France. In addition, it is also listed as Intangible Cultural Heritage of Humanity by UNESCO for its Procession of the Holy Blood.
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use Apache MXNet's to build and deploy a deep learning model for image recognition.
By the end of this training, participants will be able to:
Install and configure Apache MXNet and its components.
Understand MXNet's architecture and data structures.
Use Apache MXNet's low-level and high-level APIs to efficiently build neural networks.
Build a convolutional neural network for image classification.
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