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Data Science’s Most Used, Confused, and Abused Jargon
As data science has spread through the mainstream, so too has a dense vocabulary of ill-defined jargon. In a split-personality post, we offer several perspectives on many of data science's most confused terms.
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(Deep Learning’s Deep Flaws)’s Deep Flaws
Recent press has challenged the hype surrounding deep learning, trumpeting several findings which expose shortcomings of current algorithms. However, many of deep learning's reported flaws are universal, affecting nearly all machine learning algorithms.
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The High Cost of Maintaining Machine Learning Systems
Google researchers warn of the massive ongoing costs for maintaining machine learning systems. We examine how to minimize the technical debt.
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MetaMind Competes with IBM Watson Analytics and Microsoft Azure Machine Learning
While Microsoft and IBM rush to bring data science and visualization to the masses, MetaMind follows another path, offering deep learning as a service.
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Differential Privacy: How to make Privacy and Data Mining Compatible
Can privacy coexist with machine learning and data mining? Differential privacy allows the learning of general characteristics of populations while guaranteeing the privacy of individual records.
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IBM Watson Analytics vs. Microsoft Azure Machine Learning (Part 1)
IBM Watson Analytics prototype seeks to abstract away data science, taking ordinary natural language queries and answering them based on the content of uploaded datasets. Microsoft Azure Machine Learning goes the opposite route, streamlining existing data mining methodology for fast results and integration with MS's other cloud services.
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Geoff Hinton AMA: Neural Networks, the Brain, and Machine Learning
In a wide-ranging Q&A, Geoff Hinton addresses the future of deep learning, its biological inspirations, and his research philosophy.
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