These slides accompanied a demo of Deeplearning4j at the SF Data Mining Meetup hosted by Trulia.
http://www.meetup.com/Data-Mining/events/212445872/
Deep-learning is useful in detecting identifying similarities to augment search and text analytics; predicting customer lifetime value and churn; and recognizing faces and voices.
Deeplearning4j is an infinitely scalable deep-learning architecture suitable for Hadoop and other big-data structures. It includes a distributed deep-learning framework and a normal deep-learning framework; i.e. it runs on a single thread as well. Training takes place in the cluster, which means it can process massive amounts of data. Nets are trained in parallel via iterative reduce, and they are equally compatible with Java, Scala and Clojure. The distributed deep-learning framework is made for data input and neural net training at scale, and its output should be highly accurate predictive models.
The framework's neural nets include restricted Boltzmann machines, deep-belief networks, deep autoencoders, convolutional nets and recursive neural tensor networks.
Finally, Deeplearning4j integrates with GPUs. A stable version was released in October.
Avoid Git Bloat and Submodule Hell with Git FusionPerforce
See the webinar: http://perforce.com/resources/presentations/webinars/dev-talk-avoid-git-bloat-submodule-hell
How do you avoid the burden of working with bloated, monolithic Git repositories and sidestep the complexity of submodules?
Perforce Git Fusion lets you incrementally break up large Git repositories into small functional repositories. You can break off chunks for different teams and build localized processes around those smaller units, while also maintaining the "big" build. That way, you’ve got a pathway for migrating to a much more Agile delivery system.
Join Perforce engineer and Git user, Russ Tremain, as he discusses how Git Fusion can help you…
* Avoid the hassle of splitting up large repos using standard Git tools
* Create new repositories—either in Git or in Perforce—by picking and choosing the content you want
* Prepare for your growth in products and teams
PRESENTED BY: Russ Tremain
Build Engineer, Perforce
Russ Tremain is a veteran Software Engineer who currently specializes in advanced automation frameworks for software build, test, and release. He holds degrees in Computer and Information Science, and Information Studies from UC Santa Cruz and UC Berkeley, respectively. Russ has authored and actively participates in several open source projects, including the "Cado" language, which he uses to develop structured source code transformations.
This PowerPoint was included in the presentation given by David McMillin at Atlantic University's "Parapsychology and Consciousness Conference" that took place in Virginia Beach, Virginia, USA, from October 14th-16th, 2011.
Issues in the case study of "Global Knowledge Management at Danone" has been discussed. The issues are:
1- Creating knowledge cultures
2- Knowledge application
3- To extend the Networking Attitude
Shortest path search for real road networks and dynamic costs with pgRoutingantonpa
This presentation will show the inside and current state of pgRouting
development. It will explain the shortest path search in real road
networks and how the data structure is important for getting better
routing results. We will show how you can improve the quality of the search with dynamic costs and make the result look closer to the reality. We will demonstrate the way of using pgRouting together with other Open Source tools. Also you will learn about difficulties and limitations of implementing routing functionality in GIS applications, the difference between algorithms and their performance.
pgRouting is an extension of PostgreSQL and PostGIS. A predecessor of
pgRouting - pgDijkstra, written by Sylvain Pasche from Camptocamp, was
extended by Orkney (Japan) and renamed to pgRouting, which now is a part of the PostLBS project.
pgRouting can perform:
* shortest path search (with 3 different algorithms)
* Traveling Salesperson Problem solution (TSP)
* driving distance geometry calculation