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Deep Learning with Caffe

( Duration: 5 Days )

This Deep Learning with Caffe training course explores the application of Caffe as a Deep learning framework for image recognition using MNIST as an example.

By attending Deep Learning with Caffe workshop, delegates will learn 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

The Deep Learning with Caffe class is ideal for:

  • Deep Learning researchers and engineers interested in utilizing Caffe as a framework.




  • 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

Encarta Labs Advantage

  • One Stop Corporate Training Solution Providers for over 6,000 various courses on a variety of subjects
  • All courses are delivered by Industry Veterans
  • Get jumpstarted from newbie to production ready in a matter of few days
  • Trained more than 50,000 Corporate executives across the Globe
  • All our trainings are conducted in workshop mode with more focus on hands-on sessions

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Contact us for delivering this course as a public/open-house workshop/online training for a group of 10+ candidates.