Deep Learning for Vision with Caffe Treningskurs

Kurskode

caffe

Varighet

21 timer (vanligvis 3 dag inkludert pauser)

Krav

None

Oversikt

Caffe er en dyp læringsramme laget med uttrykk, hastighet og modularitet i tankene.

Dette kurset utforsker anvendelsen av Caffe som en Deep læringsramme for bildegjenkjenning ved bruk av MNIST som eksempel

Publikum

Dette kurset passer for Deep Learning forskere og ingeniører som er interessert i å bruke Caffe som rammeverk.

Etter fullført kurs vil delegatene kunne:

  • forstå Caffe sin struktur og distribusjonsmekanismer
  • utføre installasjons- / produksjonsmiljø / arkitekturoppgaver og konfigurasjon
  • vurdere kodekvalitet, utfør feilsøking, overvåking
  • implementere avansert produksjon som treningsmodeller, implementere lag og logging

Machine Translated

Kursplan

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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