Zedge | Data Scientist and Android SWE positions | Trondheim, Norway | ONSITE, FULL-TIME | EU/EEC work permit/visa required | https://corp.zedge.net/join-our-playground
Zedge (NYSE MKT: ZDGE) provides personalization apps/services (primarily on Android and iOS) for ~30 million monthly active users.
On the data science side we use Hadoop and (increasingly) Clickhouse for analytics in combination with both using and developing Deep Learning (Keras/Tensorflow) for content analysis (e.g. audio and images) and content discovery (e.g. recommender systems and search). We are looking for data scientist candidates that also have solid software engineering skills, a doer mindset and an aptitude to learn.
Blog posts related to some of the things we've been looking into related to Deep Learning:
Zedge | Trondheim, Norway and New York City, NY| Full Time/Onsite
Zedge (NYSE Market: ZDGE) is a content platform, and global leader in smartphone personalization, with more than 200 million app installs and 30 million monthly active users.
My experience is that iPhone 6 GPU can be up to 50-70 times faster than the CPU - for single precision floating point (i.e. Swift running on the CPU, and Metal on the GPU). See http://memkite.com/blog/2014/12/18/gpgpu-performance-of-swif... for an example (comparison with Accelerate framework)
Yes, I know the bibliography is a bit sparse right now, but we're gradually increasing its coverage. Will also add more annotations to it (per category), e.g. areas where deep learning could be applied but is lightly or not at all applied yet.
The Envisage Research Project - http://envisage-project.eu - is working on developing formal methods for software engineering for the cloud, ref: http://envisage-project.eu/wp-content/uploads/2013/10/Envisa...
"ENVISAGE will create a development framework based on formal methods to include resources and resource management into the design phase in software engineering for the cloud. This will improve the competitiveness of SMEs and profoundly influence business ICT strategies in virtualized computing"
I'm probably not clear enough in the blog post: The intention is to use Twitter as a news source, i.e. crawl and index top URIs (which can be any type of news, blog and other content). The underlying idea is that URIs on tweets give a good sample of _all_ overall knowledge production per day.
There are also of course some benefits of having all the interaction happen between you and your device that I haven't talked about in the blog post, e.g. increased privacy (no data collection), lower latency (disk seek on a mobile or tablet SSD - 100 microseconds) - is roughly 1000 times lower than the latency of accessing 3G or 4G can be (up to hundreds of milliseconds)
Ran Raz proved that Matrix Inversion is O(n^2 lg n), ref: Ran Raz. On the Complexity of Matrix Product. SIAM Journal on Computing,
32(5):1356–1369, 2003.
Regarding even faster operations, it has been hypothetized that all matrices are Toeplitz or Hankel (which have O(n lg n) algorithms), ref: D. S. Mackey, N. Mackey, and S. Petrovic. Is Every Matrix Similar to a Toeplitz
Matrix. Linear Algebra & its Applications, 297:87105, 1999.
But that was proved to not be the case:
T. Amdeberhan and G. Heinig - http://www-math.mit.edu/~tewodros/georgmemoriam.pdf
Zedge (NYSE MKT: ZDGE) provides personalization apps/services (primarily on Android and iOS) for ~30 million monthly active users.
On the data science side we use Hadoop and (increasingly) Clickhouse for analytics in combination with both using and developing Deep Learning (Keras/Tensorflow) for content analysis (e.g. audio and images) and content discovery (e.g. recommender systems and search). We are looking for data scientist candidates that also have solid software engineering skills, a doer mindset and an aptitude to learn.
Blog posts related to some of the things we've been looking into related to Deep Learning:
- https://corp.zedge.net/developers-blog/creative-ai-on-the-ip...
- https://corp.zedge.net/developers-blog/deep-learning-at-zedg...
(I am leading the data science team)