Caffe Training in Reading

Caffe Training in Reading

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.

Reading TVP

Reading Tames Valley Park
400 Thames Valley Park Drive Thames Valley Park
Reading, BRK RG6 1PT
United Kingdom
Berkshire GB
Reading TVP
The Thames Valley Park business centre is in a prestigious, modern 200-acre business park and nature reserve idyllically positioned close to the south bank of the River Thames on the east side of Reading. The city has a significant historical involvement in the information technology industry and... Read more

Caffe Course Events - Reading

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
caffe Deep Learning for Vision with Caffe Reading TVP 21 hours Mon, 2018-06-18 09:30 £3900 / £4695
caffe Deep Learning for Vision with Caffe Reading TVP 21 hours Mon, 2018-08-20 09:30 £3900 / £4695
caffe Deep Learning for Vision with Caffe Reading TVP 21 hours Tue, 2018-10-16 09:30 £3900 / £4695

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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