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 Kuala Lumpur or in NobleProg corporate training centers in Kuala Lumpur.
NobleProg -- Your Local Training Provider
Kuala Lumpur KL Sentral
1 Sentral, Level 16, Jalan , Kuala Lumpur, malaysia, 50470
The 1 Sentral Centre is located in the northwest corner of Kuala Lumpur Sentral, a prime location in the heart of the city. A...
The 1 Sentral Centre is located in the northwest corner of Kuala Lumpur Sentral, a prime location in the heart of the city. Although 1 Sentral is just outside the central business district, Kuala Lumpur Sentral is known as a transportation hub and as one of the designated Multimedia Supercorridor (MSC) locations. A Government designated zone, the MSC is designed to leapfrog Malaysia into the information and knowledge age. The zone aims to attract companies with tax breaks and facilities such as high speed internet and proximity to the local international airport. 1 Sentral is a 30-storey office tower covering an acre and is equipped with facilities such as mini auditorium, gymnasium and recreational area. Its architecture has integrated a variety of design techniques that make it eco-friendly and aesthetically-pleasing. Restaurants, top hotels and shopping centres are all within a five- to 10-minute walk from the centre
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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