Deep Learning for Vision with Caffe Training Course

Nobleprog Limited

Course

In City Of London

Price on request

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Description

  • Type

    Course

  • Location

    City of london

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

Facilities

Location

Start date

City Of London (London)
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Token House, 11-12 Tokenhouse Yard, EC2R 7AS

Start date

On request

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Course programme

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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Deep Learning for Vision with Caffe Training Course

Price on request

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