Deep Learning for Vision with Caffe Training Course

Course Code

caffe

Duration

21 hours (usually 3 days including breaks)

Requirements

None

Overview

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

Course Outline

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

 

 

Testimonials

★★★★★
★★★★★

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