Caffe Tutorial

    In one sip, Caffe is brewed for

    • 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.
    • 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.

    and these principles direct the project.

    • Nets, Layers, and Blobs: the anatomy of a Caffe model.
    • : the essential computations of layered compositional models.
    • Loss: the task to be learned is defined by the loss.
    • : 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.
    • : how to caffeinate data for model input.

    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.

    The 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.

    These recent academic tutorials cover deep learning for researchers in machine learning and vision:

    • Deep Learning Tutorial by Yann LeCun (NYU, Facebook) and Marc’Aurelio Ranzato (Facebook). ICML 2013 tutorial.

    For an exposition of neural networks in circuits and code, check out by Andrej Karpathy (Stanford).