AI and Data Science
Data Science covers many topics including machine learning, statistics, artificial intelligence, and other techniques that attempt to learn from data.
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- Written by Douglas Eadline
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From the this article was written by a puny human department
There is a notion floating about that suggests machine learning with deep learning is a GPU focused application. While GPUs excel at deep learning, they are not exclusively required to teach your servers some smarts. There are many free and open tools for machine learning that use good old fashion CPUs (some can also use GPUs). Before we point out some of the tools, however, a few background comments about machine learning are in order.
Machine learning can take many forms and cover many methods one of which is deep learning. Back in the 1980's machine learning was called Artificial Intelligence (AI). As with many technologies, AI was oversold and lost credibility because promised breakthroughs never materialized. Beyond the hype, there has been steady progress with machine learning. In commercial contexts, machine learning methods may be referred to as data science (statistics), predictive analytics, or predictive modeling.
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From the machine-overlord department
Previously, we highlighted several open (and GPU optional) machine learning packages. Recently Baidu has released a beta version of their ML tools. According to the git hub page,
"The software will be released on Sept. 30 with full documentation and installation support. A pre-release version is available now for those who are eager to take a look. PaddlePaddle (PArallel Distributed Deep LEarning) is an easy-to-use, efficient, flexible and scalable deep learning platform, which is originally developed by Baidu scientists and engineers for the purpose of applying deep learning to many products at Baidu.
One of the most interesting components of this release is a step-by-step quick start in Python!
