Caffe Training in Brighton

Caffe Training in Brighton

Caffe is a deep learning framework made with expression, speed, and modularity in mind.

NobleProg onsite live Caffe training courses demonstrate through interactive discussion and hands-on practice the application of Caffe as a Deep learning framework.

Caffe training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop setup. Local Caffe training can be carried out live on customer premises or in NobleProg local training centers.

Brighton

3rd Floor, Queensberry House 106 Queens Road
Brighton, ESX BN1 3XF
United Kingdom
East Sussex GB
Brighton
This centre occupies a prominent location on Queens Road which links Brighton station to the Churchill Square shopping centre and the seafront. Queensberry House, which offers some sea views, is in the heart of the business district in this city. Brighton is a highly sophisticated and modern resort... Read more

Caffe Course Events - Brighton

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
caffe Deep Learning for Vision with Caffe Brighton 21 hours Wed, 2018-06-13 09:30 £3900 / £4500
caffe Deep Learning for Vision with Caffe Brighton 21 hours Wed, 2018-08-15 09:30 £3900 / £4500
caffe Deep Learning for Vision with Caffe Brighton 21 hours Wed, 2018-10-10 09:30 £3900 / £4500

Course Outlines

Code Name Duration Outline
caffe Deep Learning for Vision with Caffe 21 hours

Caffe is a deep learning framework made with expression, speed, and modularity in mind.

This course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example

Audience

This course is suitable for Deep Learning researchers and engineers interested in utilizing Caffe as a framework.

After completing this course, delegates will be able to:

  • understand Caffe’s structure and deployment mechanisms
  • carry out installation / production environment / architecture tasks and configuration
  • assess code quality, perform debugging, monitoring
  • implement advanced production like training models, implementing layers and logging
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