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 training can be carried out locally on customer premises in Frankfurt or in NobleProg corporate training centers in Frankfurt.
NobleProg -- Your Local Training Provider
Frankfurt am Main
NobleProg Frankfurt, Domstraße 5, Frankfurt am Main, germany, 60528
Overview
Our training facilities are located at Domstrasse 5 in Frankfurt am Main. Located in t...
Overview
Our training facilities are located at Domstrasse 5 in Frankfurt am Main. Located in the old town of the Main metropolis, our premises offer enough space for successful training courses in the heart of the city, within walking distance of the Dom/Römer underground station.
Arrival
The NobleProg training facilities are located in the heart of Frankfurt's Old Town, near the Imperial Cathedral. With the Dom/Römer subway station, there is a central subway stop in the immediate vicinity.
Parking
Parking is allowed in the vicinity of our training rooms, even if the search for a parking space can be a bit more time-consuming depending on the date. Next door there is also the Dom/Römer multi-storey car park.
Local Services
Not far from the cathedral, in the direction of downtown Frankfurt, there are numerous restaurants and bars and within a 5 minute radius there are several hotels.
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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