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. Adelaide onsite live Apache MXNet trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
Adelaide City Central
Level 5, City Central, Tower 2, Adelaide, Australia, 5000
The Adelaide City Central Centre is in the heart of the city in the legal and financial district. Situated in a premium offic...
The Adelaide City Central Centre is in the heart of the city in the legal and financial district. Situated in a premium office building with outstanding green credentials, the centre is in one of the most technologically advanced buildings in Australia. Tenants in the building include several blue chip companies while the surrounding area is a hub for law firms, banks and insurance companies. Other important businesses in Adelaide include electronics, media, oil and defence. The building which has floor to ceiling windows has the latest technology in place to save substantially on energy, water and air conditioning. As well as offices, the building has a retail area, landscaped plazas and restaurants. The centre is within walking distance of many of Adelaide's attractions including the River Torrens, Adelaide Central Markets and Adelaide Oval.
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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