If you’re coding a deep neural network,
using a Deep Learning software library is a sure-fire way to simplify the development
process. Rather than re-invent the wheel, you can take advantage of well-tested code
that was created by experts in the field. Let’s take a closer look. If you aren’t a developer or if you’re
unfamiliar with the term, a library is a premade set of functions and modules that you can
call through your own programs. Libraries are typically created by highly-qualified
software teams, and popular libraries are regularly maintained. Many libraries are open-source,
and are surrounded by big communities that provide support and contribute to the codebase.
Deep Learning has plenty of great libraries available, several of which were created by
key people in the field. Have you ever tried to code your own deep
net? Did you use a library to help simplify the process? Please comment and share your
thoughts. If you’re building a commercial app that
requires the use of a deep net, your best bet is to use a commercial-grade library like
deeplearning4j, Torch, or Caffe. For educational or scientific projects, you should use a library
like Theano. Another notable library is deepmat, and there are many others. In the next video, we’ll start by looking
at the Theano library.