沒有
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
安裝
- 碼頭工人
- Ubuntu
- RHEL / CentOS / Fedora 安裝
- 窗戶
Caffe 概述
- Nets、Layers 和 Blobs：Caffe 模型的剖析。
- 前向/後向：分層組合模型的基本計算。
- 損失：要學習的任務由損失定義。
- 求解器：求解器協調模型優化。
- 層目錄：層是建模和計算的基本單元 - Caffe 的目錄包括用於最先進模型的層。
- 介面：命令行、Python 和 MATLAB Caffe。
- 數據：如何為模型輸入的數據添加咖啡因。
- 含咖啡因的捲積：Caffe 如何計算卷積。
新模型和新代碼
- 使用快速 R-CNN 進行檢測
- 使用 LSTM 的序列和使用 LRCN 的視覺 + 語言
- 使用FCN進行圖元級預測
- 框架設計與未來
例子：
- MNIST
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