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. Madrid onsite live Apache MXNet trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Madrid - Chamartín Business Area
NobleProg Madrid, Street Bausá 27, Madrid, spain, 28033
Madrid is a municipality and city of Spain is the most populated city of the country, with 3 165 235 inhabitants. On the econ...
Madrid is a municipality and city of Spain is the most populated city of the country, with 3 165 235 inhabitants. On the economic level, it is the fourth richest city in Europe, after London, Paris and Moscow. It is an influential cultural center and has museums of international reference, among which stand out the Prado Museum, the National Museum of Art Reina Sofia, Thyssen-Bornemisza and CaixaForum Madrid.
Being one of the largest cities in all of Spain and one of the most populated, it is characterized by having an extensive transport network consisting of rail, metro and bus lines, in addition to its airport.
Located in the business area of Chanmartin five minutes from Plaza Castilla and less than ten minutes from the airport Madrid Barajas and Madrid downtown area.
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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