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fharrell.com | ||
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jaydaigle.net
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| | | | | This is the second-part of a three-part series on hypothesis testing. Today we'll look at the way we do hypothesis testing in practice, and how it tends to fail. Modern researchers use hypothesis testing as a tool to develop knowledge, but it's really a tool for making decisions, and so it encourages us to draw strong conclusions from weak evidence. It also encourages us to view studies that don't reject the null hypothesis as failures, which leads even honest and dedicated researchers to do shoddy resea... | |
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gameswithwords.fieldofscience.com
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| | | | | In this week's New Yorker , Jonah Lehrer shows once again just how hard it is to do good science journalism if you are not yourself a scient... | |
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www.fharrell.com
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| | | | | This article explains why for decision making the original idea of null hypothesis testing never delivered on its goal. | |
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programmathically.com
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| | | Sharing is caringTweetIn this post, we develop an understanding of why gradients can vanish or explode when training deep neural networks. Furthermore, we look at some strategies for avoiding exploding and vanishing gradients. The vanishing gradient problem describes a situation encountered in the training of neural networks where the gradients used to update the weights [] | ||
