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 Aberdeen or in NobleProg corporate training centers in Aberdeen.
The Berry Street Centre occupies a three-storey 1950s building, which has recently been refurbished to a high specification. ...
The Berry Street Centre occupies a three-storey 1950s building, which has recently been refurbished to a high specification. It is in a prominent and convenient location in the heart of Aberdeen's busy and thriving retail and business area and close to all amenities. The off-shore oil capital of Europe, Aberdeen is known for its outward looking, pioneering spirit. Over the last 30 years, the city has reinvented itself from a regional port dependent upon fishing, farming and tourism, to become the centre of the UK's energy industry and one of Europe's most enterprising regions. It boasts the second highest rate of business formation and the highest concentration of knowledge based businesses in Scotland and is a founding member of the World Energy Cities Partnership. Other important sectors in the city's economy include tourism and distribution, the public sector, financial services, manufacture, agriculture and food processing, IT and construction.
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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