DIY Deep Learning for Vision- a Hands-On Tutorial with Caffe

Examples:
Logistic Regression (in Python):http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/hdf5_classification.ipynb
Learn LeNet on MNIST:http://caffe.berkeleyvision.org/gathered/examples/mnist.html







Fine-tuning CaffeNet for Style Recognition on “Flickr Style” Data
http://tutorial.caffe.berkeleyvision.org/gathered/examples/finetune_flickr_style.html

























Caffe Demo : http://demo.caffe.berkeleyvision.org/
Feature Visualization :
http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/filter_visualization.ipynb



How to transform models in Caffe:
http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/net_surgery.ipynb
Related projects:

R-CNN: Regions with CNN
Ross Girshick et al.Rich feature
hierarchies for accurate object detection and semanticsegmentation. CVPR14.
http://nbviewer.ipython.org/github/BVLC/caffe/blob/dev/examples/detection.ipynb
Full scripts:
https://github.com/rbgirshick/rcnn
Visual Style Recognition:
Karayev et al.Recognizing Image Style.
BMVC14. Caffe fine-tuning example
[align=left]Demo:http://demo.vislab.berkeleyvision.org/[/align]
[align=left]Latest Roast:[/align]
Model Zoo: https://github.com/BVLC/caffe/wiki/Model-Zoo
[align=left]- BVLC reference models[/align]
- VGG Devil + ILSVRC14 modelsin
the zoo
- Network-in-Network / CCCP modelin
the zoo

[align=left]Caffe + cuDNN[/align]

[align=left]Parallel / distributed training across CPUs, GPUs, and cluster nodes[/align]
[align=left]https://github.com/BVLC/caffe/pull/1148[/align]

参考：DIY Deep Learning for Vision with Caffe slides

https://docs.google.com/presentation/d/1UeKXVgRvvxg9OUdh_UiC5G71UMscNPlvArsWER41PsU/preview?sle=true#slide=id.p