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.
In one sip, Caffe is brewed for
and these principles direct the project.
For a closer look at a few details:
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 Tutorial on Deep Learning for Vision 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.
A broad introduction is given in the free online draft of Neural Networks and Deep Learning by Michael Nielsen. In particular the chapters on using neural nets and how backpropagation works are helpful if you are new to the subject.
These recent academic tutorials cover deep learning for researchers in machine learning and vision:
For an exposition of neural networks in circuits and code, check out Understanding Neural Networks from a Programmer’s Perspective by Andrej Karpathy (Stanford).