- Docker
- Ubuntu
- RHEL / CentOS / Fedora installation
- Windows
- 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.
- Detection with Fast R-CNN
- Sequences with LSTMs and Vision + Language with LRCN
- Pixelwise prediction with FCNs
- Framework design and future
- MNIST
None
21 hours (usually 3 days including breaks)
