dusenberrymw

I can assure you that is not the case. :) We have a very active & growing team focused on the project, and we're all quite excited about it. If you're interested in contributing, please jump in or reach out!

Great question. I'm part of the committer team for the project at IBM, so I'll leave a few comments representing our thoughts. As a quick overview, SystemML provides an R-like DSL, called DML, consisting of linear algebra primitives (vectors, matrices), built-in functions for common functions (such as sums, means, matrix construction, etc.), UDFs, etc., as well as a compiler/optimizer engine that can generate optimized runtime plans from the same DML script for a single node (laptop), Spark, or Hadoop MapReduce. We definitely have algorithms already available as production-ready examples, but the goal of the project is to allow for declarative ML using customizable scripts written at the mathematical DSL level, rather than to provide a fixed library of algorithms at the base language level (Scala, Python, etc.). MLlib (including the newer ML API) is awesome, and provides a great set of algorithms that fit in quite well with Scala, Python (& Java). SystemML is great in that it provides the ability to run customizable, linear algebra-based ML scripts (that can be automatically optimized within the engine) on Spark. Together, it's a great combo. We also have an API for Scala that lets one embed DML into a Scala program similar in manner to how an SQL script can be embedded [http://sparktc.github.io/systemml/mlcontext-programming-guid...].

Here are our new Apache links:

https://systemml.apache.org

https://github.com/apache/incubator-systemml

Location: NC (Looking in SF Bay Area)

Remote: No

Willing to relocate: Yes, to San Francisco Bay Area

Technologies: Java, C/CUDA, Python, Machine Learning, Neural Networks, Octave/MATLAB, SQL, Prolog, Javascript, HTML5, CSS3, PHP, R, F#, Lisp

LinkedIn: https://linkedin.com/in/mikedusenberry

GitHub: https://github.com/dusenberrymw

Blog: http://mikedusenberry.com

Email: dusenberrymw@gmail.com

Overall, I'm really interested in problems at the intersection of computer science, machine learning, and medicine! Just submitted a research paper involving the use of custom neural networks for predicting CT findings in emergency department patients, and now I'm looking for a company with which to work on larger machine learning projects.

http://jekyllrb.com is a great static site generator for blogging, and can be paired with GitHub Pages for free hosting.

Great contribution and interesting read. I'll certainly be checking this method out in more depth!

Well played...

Yeah that's a great paper, and I definitely used it to learn more while I was writing this post up. If anyone else wants it, here's a direct link: [http://www.cs.ucsb.edu/~mturk/Papers/mturk-CVPR91.pdf].

I really should add a section of resources that I found useful. Thanks!

[Author here] Definitely never intended to claim that this was an original discovery; the original paper using the term is ~25 years old [http://www.cs.ucsb.edu/~mturk/Papers/mturk-CVPR91.pdf].

Nonetheless, I've found it to be an interesting concept. There is indeed a homework from the Coursera ML course for computing and visualizing eigenfaces, and the course (and the Stanford CS229 notes) discuss PCA further. I decided to explore the ideas further and distill it into a blog post specifically on eigenfaces.

Goal is for it to serve as a condensed tutorial on an interesting topic! I definitely learned a bunch writing it, and it may be interesting to others who have yet to come across to concept.