Bandwidth and general cumbersomeness of dealing with larger amounts of data with starving-startup resources. I actually spent about a decade of my career focused on image processing, and while I love it's power, I knew how much of an engineering challenge it can be at massive scale. I need to do a blog post about this, since I know my choice is a bit surprising and needs explanation.
There are ways to get it done algorithmically, however, the challenge is in getting enough data. 30,000 images is too low. you would need a few million, then just simple machine learning algorithms would work.
The latest machine learning techniques such as unsupervised deep learning might work, with millions of unlabeled images and the 30,000 labelled.
Kaggle has just closed a large Series A ($11.25m). Our early employees will help shape Kaggle's direction and grow along with the company. Regardless of the position, you should have a strong interest in data science and the intellectual curiosity to engage with competition clients from a wide variety of fields.
ONLY 40% of Physics nobels are given to jews, that leaves 60% to non-Jews. So, its not surprising that Martin O'Leary isn't Jewish. Unless his mom is and he took his dad's non-Jewish name. :)
"This competition requires participants to predict travel time on Sydney's M4 freeway from past travel time observations". This line seems to suggest that the past travel time is the most important part of the experiment; however, as one other (rrrhys) pointed out, the data is useless, since the road has changed, and the grandparent of this post mentioned sporting events affecting traffic.
All of that said, perhaps a strong model can be generated using just historical data.
