Step 0. Motivation.

I am pretty sure you have heard that Artificial Intelligence(A.I.) is doing everything cool in our days, it is helps create artwork, fights to cure cancer and disrupts world economic.

From the money perspective A.I. on the hype as well: investors are investing into A.I pretty active, and news about startups acquired by enterprises are coming everyday.

Step 1. Learn.

There are many of learning resources available at the moment. If you plan to dig to A.I. more fundamentally, instead of briefly visit neural networks, I am a huge supporter of a book by Russell and Norvig which covers most of the modern approach to A.I.

Deep learning.

There is a pretty good neural network course on coursera just started(28 of November, 2016), which could be useful if you are a newbie in the area.

If you are hearing about neural networks in nowadays most likely you are hearing it in a context of deep learning.

Deep learning, — as it stated in wikipedia, is — a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data by using a deep graph with multiple processing layers, composed of multiple linear and non-linear transformations. In other simple words, deep learning advance.

A more advanced approach to neural networks could be found at

I would recommend a fancy paid course if you would to get course that worth it.

Computer Vision

The state of computer vision is extremely strong. There are a lot of experienced professionals in the field, great papers, crowded open source projects. But most importantly, it had a crazy number of venture capitalists want to fund you. Computer vision has never been a hotter topic in academia and industry alike.

Most of a modern computer vision is driven by deep learning, all this coloring, video object recognition and labeling is performed by deep learning approach to computer vision.

One the top person in modern computer vision/deep learning is Andrej Karpathy, who is famous for winning computer vision competitions, creating models and evangelize deep learning for computer vision. I am totally recommend Karpathy’s Stanford course:

Also, following course is comprehensive:

More!

Personally, I am a huge fan of curated ‘awesome list’, here are some deep learning lists, but I am sure you can google much more of them:

2. Experiment.

In the past, there were not much options for teams to experiment and run neural networks — you had to pay crazy amounts to Amazon or even to tradition servers farms. There are a plenty of options in nowadays.

If you are just starting to play in neural world, take a look at Hetzner used hardware auction.

If you going to production, I would suggest a top choice for A.I. cloud by servers.com.

3. Get funded.

I have no doubts, if you would built a valuable A.I. product you have a problem with choosing a proper investors among many of them knocking your door everyday.

There are a long-long list of investors looking for A.I. startups. If you are European based, I would suggest to take a look at Haxus.



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