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Deep learning Tutorial with Caffe

Haosdent Huang 08/27/2015

Why Deep Learning

https://www.youtube.com/watch?v=V1eYniJ0Rnk

Play games!

Compositional Models and learn end-to-end

Hierarchy of Representations - vision: pixel, part, object - text: character, word, sentence - speech: audio, word - program: statement, function, module

Compositional Models and learn end-to-end

Back-propagation jointly learnsall of the model parameters to optimize the output for the task

xkcd: Tasks

Use Caffe !

How to use deep learning implement these tasks ?

Caffe

Demo: mnist dataset

Classifying Handwritten images

The models use when train mnist

layer { name: "pool1" type: "Pooling" bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

The models use when train mnist

Train !

caffe train xxx.prototxt

http://localhost:8888/notebooks/sharing/test_capture.ipynb

Model Zoo

• open collection of deep models to share innovation

• help disseminate and reproduce research

• bundled tools for loading and publishing models

https://github.com/BVLC/caffe/wiki/Model-Zoo

Demo: ImageNet

http://localhost:8888/notebooks/sharing/test_classification.ipynb

Open framework, models, and worked examples for deep learning - Pure C++ / CUDA architecture for deep learning - Command line, Python, MATLAB interfaces - Fast, well-tested code - Tools, reference models, demos, and recipes - Seamless switch between CPU and GPU

Prototype Train Deploy

demo.caffe.berkeleyvision.org

http://demo.caffe.berkeleyvision.org/

The models use when train mnist

The models use when train mnistFeature extraction using convolution

The models use when train mnist

Feature extraction using convolution

The models use when train mnistPooling(aggregation)

The models use when train mnist

Deep dream

https://github.com/google/deepdream/blob/master/dream.ipynb

Deep dream

Thank you

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