The key lesson of this session, presented by Lino Ramirez, is that it’s all about empowering people. Perl gives us the power to empower people.

I really enjoyed the video of a tae kwon do match as a real-world analog of software development. Try one technique, see the result (get hit); try another technique, see the result (get knocked down); ad infinitum.

There are three phases: preparation, modeling, and implementation. These phases are not linear in nature. One moves between phases as necessary to design the solution.

PDL was demonstrated in the modeling phase. Brad was pretty happy when one of the slides contained a web site address for information on PDL, which happened to be a web server at his job.

As he delved into the case studies, I started to zone out, so I have little to say about his examples of machine learning and Perl in action. I wanted to enjoy this session more, since I’ve often wanted to get back into using neural networks and other machine learning techniques in my code. Unfortunately, I just found it too difficult to follow his case studies. Still, I have some good pointers for packages that will help me sprinkle some machine learning goodness in my code.

I like his conclusion: “Perl excels at empowering people in all three phases of the development of a machine learning application.” Perl is awesome for rapid application development, which in turn gets solutions to people who need them faster.