   Deep Learning for Vision with Caffe培訓(xùn)

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