Caffe Training in Cambridge

Caffe Training in Cambridge

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.

Cambridge

Cambridge
Compass House Vision Park, Chivers Way, Histon
Cambridge, CAM CB24 9AD
United Kingdom
Cambridgeshire GB
Cambridge
Vision Park is within easy reach of Cambridge city centre and has the advantage of excellent links with the M11 and A14. Trains from London Kings Cross arrive every 30 minutes, with a 15 minute journey by taxi to the Park. Histon Railway Station is on the boundary of Vision Park, which provides... Read more

Caffe Course Events - Cambridge

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
caffe Deep Learning for Vision with Caffe Cambridge 21 hours Wed, 2018-05-23 09:30 £3900 / £4575
caffe Deep Learning for Vision with Caffe Cambridge 21 hours Wed, 2018-07-18 09:30 £3900 / £4575
caffe Deep Learning for Vision with Caffe Cambridge 21 hours Tue, 2018-09-11 09:30 £3900 / £4575

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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Outline Tag Cloud

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