Caffe is a deep learning framework developed by Berkeley AI Research (BAIR) and by community contributors. It was developed keeping expression, speed, and modularity in mind. If you need a Library for machine vision or a forecasting application, then caffe might be a good choice. This library let’s you build your own deep nets with a sophisticated set of their configuration options. You can even access pre-made nets that were uploaded to a Community Website. Let’s take a look..

The Caffe deep learning library was created by Google’s YangGQing Jia, who won an image net challenge in 2014. cafe 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 where is written in c plus plus with CUDA applications can easily switch between a CPU and a GPU as needed. Matlab and Python interfaces are also available for cafe.

With cafe you can build a deep net by configuring it’s 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 of Us layer and activation layer and a few others so you can perform 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 the model zoo. Alexnet and Googlenet are 2 popular user made nets available to the community there are also a few educational resources like demos and sides so if you’re going to use cafe it’s a great place to start. Caffè vectorises input data through a special data representation called a blob. A blob is a type of array that speeds up data analysis and provides synchronisation capabilities between a CPU and a GPU, have you ever used the cafe library in one of your own deep net projects?