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Deep learning Tutorial with Caffe
Haosdent Huang 08/27/2015
Why Deep Learning
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
Model Zoo
• open collection of deep models to share innovation
• help disseminate and reproduce research
• bundled tools for loading and publishing models
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
Prototype Train Deploy
demo.caffe.berkeleyvision.org
The models use when train mnist
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
Deep dream
Deep dream
Deep dream
Thank you