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| title: Caffe Tutorial |
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| # Caffe Tutorial |
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| Caffe is a deep learning framework and this tutorial explains its philosophy, architecture, and usage. |
| This is a practical guide and framework introduction, so the full frontier, context, and history of deep learning cannot be covered here. |
| While explanations will be given where possible, a background in machine learning and neural networks is helpful. |
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| ## Philosophy |
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| In one sip, Caffe is brewed for |
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| - Expression: models and optimizations are defined as plaintext schemas instead of code. |
| - Speed: for research and industry alike speed is crucial for state-of-the-art models and massive data. |
| - Modularity: new tasks and settings require flexibility and extension. |
| - Openness: scientific and applied progress call for common code, reference models, and reproducibility. |
| - Community: academic research, startup prototypes, and industrial applications all share strength by joint discussion and development in a BSD-2 project. |
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| and these principles direct the project. |
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| ## Tour |
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| - [Nets, Layers, and Blobs](net_layer_blob.html): the anatomy of a Caffe model. |
| - [Forward / Backward](forward_backward.html): the essential computations of layered compositional models. |
| - [Loss](loss.html): the task to be learned is defined by the loss. |
| - [Solver](solver.html): the solver coordinates model optimization. |
| - [Layer Catalogue](layers.html): the layer is the fundamental unit of modeling and computation -- Caffe's catalogue includes layers for state-of-the-art models. |
| - [Interfaces](interfaces.html): command line, Python, and MATLAB Caffe. |
| - [Data](data.html): how to caffeinate data for model input. |
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| For a closer look at a few details: |
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| - [Caffeinated Convolution](convolution.html): how Caffe computes convolutions. |
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| ## Deeper Learning |
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| There are helpful references freely online for deep learning that complement our hands-on tutorial. |
| These cover introductory and advanced material, background and history, and the latest advances. |
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| The [Tutorial on Deep Learning for Vision](https://sites.google.com/site/deeplearningcvpr2014/) from CVPR '14 is a good companion tutorial for researchers. |
| Once you have the framework and practice foundations from the Caffe tutorial, explore the fundamental ideas and advanced research directions in the CVPR '14 tutorial. |
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| A broad introduction is given in the free online draft of [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html) by Michael Nielsen. In particular the chapters on using neural nets and how backpropagation works are helpful if you are new to the subject. |
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| These recent academic tutorials cover deep learning for researchers in machine learning and vision: |
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| - [Deep Learning Tutorial](http://www.cs.nyu.edu/~yann/talks/lecun-ranzato-icml2013.pdf) by Yann LeCun (NYU, Facebook) and Marc'Aurelio Ranzato (Facebook). ICML 2013 tutorial. |
| - [LISA Deep Learning Tutorial](http://deeplearning.net/tutorial/deeplearning.pdf) by the LISA Lab directed by Yoshua Bengio (U. Montréal). |
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| For an exposition of neural networks in circuits and code, check out [Understanding Neural Networks from a Programmer's Perspective](http://karpathy.github.io/neuralnets/) by Andrej Karpathy (Stanford). |
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