dusenberrymw
Joined 12 years ago
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13
Member since
Sep 17
Recent submissions
Comment · 11 years ago
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!
Comment · 11 years ago
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:
Comment · 11 years ago
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: [email protected]
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.
Comment · 11 years ago
http://jekyllrb.com is a great static site generator for blogging, and can be paired with GitHub Pages for free hosting.
Comment · 11 years ago
Great contribution and interesting read. I'll certainly be checking this method out in more depth!
Comment · 11 years ago
Well played...
Comment · 11 years ago
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!
Comment · 11 years ago
[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.
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