Deep Learning for Vision with Caffe培訓

課程代碼

caffe

課程時長

21 時間: 同常來說包括休息是 3天

最低要求

None

概觀

Caffe是一個深刻的學習框架,以表達,速度和模塊化為基礎。

本課程以MNIST為例,探討了Caffe作為圖像識別的深度學習框架的應用

聽眾

本課程適合有興趣使用Caffe作為框架的Deep Learning研究人員和工程師。

完成本課程後,代表們將能夠:

  • 了解Caffe的結構和部署機制
  • 執行安裝/生產環境/架構任務和配置
  • 評估代碼質量,執行調試,監控
  • 實施高級生產,如培訓模型,實施圖層和日誌記錄

Machine Translated

課程簡介

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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