3,178 karma · joined September 2, 2007
DSR is the market leader in EU for machine learning training. DSR helps coders or people with significant quantitative training (e.g. science, engineering, or math graduates) ramp-up rapidly for a data science career - arguably the fastest-growing, highest-demand profession. Our participants have an average of 5 years of industry experience.
DSR is a 3-month, in-person, rigorous, and full-time/intensive course in the startup-capital of Europe: Berlin. You’ll learn software engineering, data science, business analysis and communication faster and more deeply with mentors doing code reviews and pair-programming - all on real-world data and problems. You will develop a portfolio project, demonstrating you can own a business problem, solve it, and communicate why your results are definitive.
You bring your training, tuition, and drive to master our curriculum alongside our world-class mentors and partners. Towards the end we provide a networking event with top-tier technology companies, where you will show off your new skills and portfolio project, likely leaving with a career-changing job.
http://tibleiz.net/code-browser/
More powerful overall. It implements elastic tabstops too! Little unknown gem. good for outlining a book/paper also.
It makes me sad, because I live on linux and notetaking apps there are mediocre. The best I could find is keepnote.
Onenote is years ahead of everything else, but I don't want the vendor lock-in and the binary files that it produces.
Evernote moved to .NET 3.5 so no wine support. Bah!
Actually, this view is probably the best entry point: http://cran.r-project.org/web/views/HighPerformanceComputing...
Where it's not so strong, of course, is in terms of web frameworks. Nothing that I could find, and, considering the scope of the language, it's allmost unreasonable to expect it.
The sooner this 'clicks' on any marketing high-up at Apple, the better.
I have + ~5 keyboards. Using a kinesis contour now.
Downsides include poor programming tools/debugger and pass-by-value. Still, it's one of my favorite languages.
One important thing that you are missing: you would not want to startup with someone you don't know from before, and have complete, blind trust in. And you cannot 'hire' a cofounder. You hire workers, but a cofounder doesn't work for you, by definition.
It may not be good news, but it's news and something people here should know. How it gets to the top of the front page with 2 votes... no idea. Slow day?
One other thing: Having an active social life can be costly, both online and offline. It's just that offline more people actually do it.
The equivalent of following hundreds of people on twitter and facebook in 'fleshspace' is to live in a house with guests (and flatmates) changing constantly. There are other situations in which one is forced to be hypersocial (maybe working at a bar? but that doesn't lead to intimacy), but this one I know first-hand. As anyone who has done the house thing can attest, it can be draining. You spread your attention to others very thin. And even so, the chances of meeting people you really like are not very high.
The web learns. Not only by humans adding data consciously, but also by sensors. This is the hidden treasure on the mobile web. They call it the sensor revolution. Quote: "Today’s smartphones contain microphones, cameras, motion sensors, proximity sensors, and location sensors (GPS, cell-tower triangulation, and even in some cases, a compass)"
Data is capital, and sensors are the new way of getting data. Best quote: "Think of sensor-based applications as giving you superpowers"
There's a huge opportunity building structured data from unstructured data. Best quote: "It’s also possible to give structure to what appears to be unstructured data by teaching an application how to recognize the connection between the two."
Another big theme is connecting real life with the searchable web. Best quote: People talk about "the Internet of Things" assume that what will get us there is the combination of ultra-cheap RFID and IP addresses for everyday objects (…). Instead, we can make do with bar codes, tags on photos, and other "hacks" that are simply ways of brute-forcing identity out of reality.
Winning applications are designed to get better the more people use them
As a side note, I would encourage anyone here to learn R (www.r-project.org). Data analysis is flourishing in a world where new, interesting data is so easy to collect. Quote: "Data analysis, visualization, and other techniques for seeing patterns in data are going to be an increasingly valuable skillset. Employers take notice."
Real-time is huge. We need machine learning to be applied here to make sense of streams of data.
Quote: "The Web is no longer an industry unto itself – the Web is now the world." We need to solve real problems in the real world.
The article concludes with some examples: Government 2.0 (Obama champaign), crowdsourcing for personalized medicine.
The current scene of statistical semantics is very active right now. I'm not sure that the SVD is easier to implement than the probabilistic versions (LDA, topics model). The svd code LSA uses required sparse matrices, and the code runs using the Lanczos algorithm; it also needs to place the entire matrix in memory at some point. This limits the scale of the corpus you can deal with. The probabilistic versions are iterative, and while they may take more CPU and time, they are not memory bounded.