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Deep Learning for Vision with Caffe Training Course

Course

Online

£ 3,000 VAT inc.

Description

  • Type

    Course

  • Methodology

    Online

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

About this course

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Subjects

  • Monitoring
  • Windows
  • Installation
  • Fedora
  • Quality
  • Design
  • Art
  • Image
  • Quality Training
  • Production

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

Additional information

21 hours (usually 3 days including breaks)

Deep Learning for Vision with Caffe Training Course

£ 3,000 VAT inc.