Inspired by this blogpost: https://blog.openai.com/evolution-strategies/ I didn't see any projects that showed a basic example of this in Keras so I figured I'd give it a shot: https://gist.github.com/nicksam112/00e9638c0efad1adac878522cf172484 It's able to solve several gym environments such as Cartpole and BipedalWalker just by running on a single laptop CPU.
The creative aspects of machine learning are overshadowed by visions of an autonomous future, but machine learning is a powerful tool for communication. Most machine learning in today’s products is related to understanding.
Shout out to Icko_ (https://www.reddit.com/user/Icko_) and chub79 (https://www.reddit.com/user/chub79) for helping me! https://github.com/danielhanchen/sciblox. Check https://danielhanchen.github.io/ for demonstration. So what's new?
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The National Basketball Association is hosting an Analytics Hackathon.
Application are accepted until August 6, 2017 and the actual competition occurs on September 23-24, 2017. The competition has 2 focus areas:
The prizes for winning consist of:
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I do have some very detailed questions about the out-of-bag error in the random forest algorithm, which I couldn't answer after some research. My understanding: OOB error is the error resulting in going through each sample and calculating the accuracy by a vote of those trees, which did not get that sample in the bootstrapping procedure and are therefore not influenced by that very sample. This is done for every sample to estimate the overall accuracy.
Analytics-as-a-Service is the combination of analytics software and cloud technology. Instead of hosting any analytics software on premises using your own servers, you use a ready-to-go solution that is easy to deploy and most of the time has a pay-as-you-go payment system. It is part of a larger ‘as-a-Service’ solutions such as ‘Software-as-a-Service’ or ‘Platform-as-a-Service’. Thanks to the advancements made by well-known hosting providers such as AWS and Microsoft Azure, Analytics-as-a-Service has really taken off in the past years and is here to stay.
A light and short note on a dense subset of a large space…
There’s increasing interest in the very happy problem of why Deep Learning methods generalize so well in real-world usage. After all,
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