Caffe Training in Leeds

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

Leeds

No 2 Wellington Place
Leeds LS1 4AP
United Kingdom
GB
Leeds
This centre is positioned in the middle of Leeds in Wellington Place, which covers 21 acres and will ultimately provide over three million square feet of...Read more

Caffe Course Events - Leeds

Code Name Venue Duration Course Date PHP Course Price [Remote / Classroom]
caffe Deep Learning for Vision with Caffe Leeds 21 hours Mon, 2018-03-12 09:30 £3300 / £3900
caffe Deep Learning for Vision with Caffe Leeds 21 hours Wed, 2018-05-02 09:30 £3300 / £3900
caffe Deep Learning for Vision with Caffe Leeds 21 hours Wed, 2018-06-27 09:30 £3300 / £3900

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

Installation

  • Docker
  • Ubuntu
  • RHEL / CentOS / Fedora installation
  • Windows

Caffe Overview

  • Nets, Layers, and Blobs: the anatomy of a Caffe model.
  • Forward / Backward: the essential computations of layered compositional models.
  • Loss: the task to be learned is defined by the loss.
  • Solver: the solver coordinates model optimization.
  • Layer Catalogue: the layer is the fundamental unit of modeling and computation – Caffe’s catalogue includes layers for state-of-the-art models.
  • Interfaces: command line, Python, and MATLAB Caffe.
  • Data: how to caffeinate data for model input.
  • Caffeinated Convolution: how Caffe computes convolutions.

New models and new code

  • Detection with Fast R-CNN
  • Sequences with LSTMs and Vision + Language with LRCN
  • Pixelwise prediction with FCNs
  • Framework design and future

Examples:

  • MNIST

 

 

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