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  • If you need a library for a machine vision or a forecasting application, then Caffe might

  • be a good choice. This library lets you build your own deep nets with a sophisticated set

  • of layer configuration options. You can even access premade nets that were uploaded to

  • a community website. Let’s take a look.

  • The Caffe Deep Learning Library was created by Google’s Yangqing Jia, who won an ImageNet

  • Challenge in 2014.

  • Caffe was originally designed for machine vision tasks, so it’s well-suited for convolutional

  • nets. However, recent versions of the library provide support for speech and text, reinforcement

  • learning, and recurrent nets for sequence processing. Since the library is written in

  • C++ with CUDA, applications can easily switch between a CPU and a GPU as needed. Matlab

  • and Python interfaces are also available for Caffe.

  • With Caffe, you can build a deep net by configuring its hyper-parameters. In fact, the layer configuration

  • options are very sophisticated. You can create a net with many different types of layers,

  • such as a vision layer, a loss layer, an activation layer, and a few others. So each layer can

  • perform a different function or take on a different role. This flexibility allows you

  • to develop extremely complex deep nets for your application. Caffe is supported by a

  • large community where users can contribute their own deep net to a repository known as

  • theModel Zoo”. AlexNet and GoogleNet are two popular user-made nets available to

  • the community. There are also a few educational resources like demos and slides, so if youre

  • going to use Caffe, it’s a great place to start.

  • Caffe vectorizes input data through a special data representation called a “blob”. A

  • blobis a type of array that speeds up data analysis and provides synchronization

  • capabilities between a CPU and a GPU.

  • Have you ever used the Caffe library in one of your own Deep Net projects? Please comment

  • and share your experiences.

If you need a library for a machine vision or a forecasting application, then Caffe might

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B1 US library layer blob cpu deep learning configuration

Caffe - Ep. 20 (Deep Learning SIMPLIFIED)

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    alex posted on 2016/05/07
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