Caffe Training in London

Caffe Training in London

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.

London, Hatton Garden

London, Hatton Garden
Etc. Venues 51-53 Hatton Garden
London , LND EC1N 8HN
United Kingdom
London GB
London, Hatton Garden
Overview Located near Farringdon and Chancery Lane. A stylish and impressive eight-storey Art Deco building ideal for hosting a range of training, conferences and meetings near Farringdon and Chancery Lane stations. Directions The Hatton is located in London's Diamond District in Hatton Garden. By... Read more

Caffe Course Events - London

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
caffe Deep Learning for Vision with Caffe London, Hatton Garden 21 hours Wed, 2018-06-06 09:30 £3900 / £5025
caffe Deep Learning for Vision with Caffe London, Hatton Garden 21 hours Mon, 2018-08-06 09:30 £3900 / £5025
caffe Deep Learning for Vision with Caffe London, Hatton Garden 21 hours Mon, 2018-10-01 09:30 £3900 / £5025

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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