[fchollet]

Deep convolutional neural nets are the staple method for companies doing computer vision at scale. Google, Facebook, Yahoo, and Baidu make extensive use of them. In 2014 they definitely deserve their place on the shortlist of "pretty standard machine-learning methods". They are the current state of the art for visual recognition in particular (see the results on ImageNet from the past few years).

They have also been commoditized by libraries such as Theano (Python) and Torch (Lua). Google and Facebook use their own tools based on Torch.

My own version of the shortlist would be: Logistic regression, KNN, random forests, SVM, and deep convnets.

[agibsonccc]

This is why I'm starting skymind[1] to address. Many of these companies are using cutting edge techniques without a focus on usability (since they know this stuff). The hope here is to create an off the shelf package people can just use with only knowledge of fairly conventional machine learning techniques while also avoiding the problems of having to program in java (which isn't realistic in data science) or even lua.

Many people could benefit from a neural nets ability to create good features for itself, but it's hard to use in a practical setting. That being said over the next few years I believe this can change.

[1]: http://wired.com/2014/06/skymind-deep-learning/

[fchollet]

I like the idea a lot. Just trying to understand this better: it seems like your company is entirely about selling consulting services, yet your stated goal is to "give people machine learning without them having to hire a data scientist". What's your path to that goal?

[agibsonccc]

In this case, being that on staff data scientist for them.

Many companies only need a one off model to set themselves up for some sort of baseline data product. This can also be training for them on using machine learning for their problem solving.

The goal isn't necessarily to totally supplant data scientists (love press sensationalism), but to help enable companies to build easy to use models in their apps.

This can also map to saving data scientists time by not necessarily "skipping" the feature extraction part (which they can with deep learning and still do reasonably well) but allowing them to just use a fairly good machine learning model out of the box to use as a baseline.

The great thing about machine learning is the ability to mix different techniques. Google's voice detection is a great example of this. They use neural nets for feature extraction and hidden markov models for final translation of speech to text.

I think deep learning (if wrapped in the right apps or sdks) with the auto feature extraction alongside then specifying say: a "task". This task could be predicting a value, labeling things, or even compression of data[1] would allow companies to not focus on machine learning, but on straight problem solving.

The idea would be once they are familiar enough with how to feed data in to the system, and specifying a "task", they can do a lot of machine learning by themselves without having to think too much about the problem they are solving (what features work best given the data I have?)

[1] http://www.slideshare.net/agibsonccc/ir-34811120

[mattrepl]

Interesting that none of the methods in either short list are inherently probabilistic.