terrabytes

I remember seeing them on Yelp a couple of months ago and they had terrible ratings (1 star). It seems to have jumped up to 3 stars now, but a surprisingly large number of them were written in the last couple of weeks. Hmm...

Suppose that data in quantities orders of magnitude greater than that used in this project was scraped from the internet and used to train a model that powers a commercial product.

Is it ethical to sell something that is dependent upon data that people might consider sensitive? Even if they willingly lent their photos to fitness organizations, it's unlikely that they would have predicted that AI would make use of their personal data in the ways it does today.

I have a Surface Book and absolutely love it. The trackpad is something I've had problems with on my previous Windows laptops. The Surface book has a fantastic trackpad, on par with Macbooks.

This is refreshing. I’ve been learning machine learning through Kaggle. Recently and I’m a bit tired with the “tuning hyperparameter” culture. It rewards people that have the pockets to spend on computing power and the time to try every parameter. I’m starting to find problems that don’t have a simple accuracy metric more interesting. It forces me to understand the problem and think in new ways, instead of going down a checklist of optimizations.

Even the SF city has been unbelievably smoky the last couple of days.

Thanks for your reply. I do agree with you, in general, and have been trying to get myself involved in more ML projects at my current work.

I have around 8 years of professional software experience (C++/C#) and have fiddled around with some rudimentary machine learning for work, like linear regression, k-means clustering, etc. I have a decent idea of how/why they work, but have fallen flat on my face when learning the theory behind more complicated algorithms, e.g. Hessians from Andrew Ng's class. In my experience, many classes tend to focus on a ground up approach. With higher level frameworks like Keras, how necessary is this?

Spot on. I struggled with the mainstream deep learning/machine learning MOOCs. I felt like they were to math heavy. However, I'm struggling on how to learn deep learning. I get polarized advice on it. Some argue that you need a degree or certificates from established MOOCs, others keep recommending me to do Kaggle challenges.

Has anyone managed to land a decent deep learning job without formal CS/machine learning training? How did you approach it?

Very neat tool! Bookmarked.

This is fantastic!

MongoDB has is a pretty good example of building a business around open source software.

The rapid pace of new GPU hardware releases is a deterrent to owning GPUs for sure. The K80s currently on AWS/GCP/Azure aren't the greatest, but they will all soon have Pascal P100s and may be even V100s in the near future. Not to mention TPUs, which will only be available on Google Cloud

I’m a Data Scientist and I work in a large-ish corp. We have a dedicated engineering team who take care of all of our infrastructure needs - we mostly focus only on data science. I would expect most medium and large enterprises to have the same setup. Why would they use FloydHub?

Yes. This is called the cocktail party problem. Though you can solve it with more traditional methods like SVD, deep learning solves this really well too. See https://arxiv.org/abs/1504.04658