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www.let-all.com | ||
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www.jeremykun.com
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| | | | | There's a well-understood phenomenon in machine learning called overfitting. The idea is best shown by a graph: overfitting Let me explain. The vertical axis represents the error of a hypothesis. The horizontal axis represents the complexity of the hypothesis. The blue curve represents the error of a machine learning algorithm's output on its training data, and the red curve represents the generalization of that hypothesis to the real world. The overfitting phenomenon is marker in the middle of the graph, before which the training error and generalization error both go down, but after which the training error continues to fall while the generalization error rises. | |
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www.unofficialgoogledatascience.com
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| | | | | by CHRIS HAULK It is sometimes useful to think of a large-scale online system ( LSOS ) as an abstract system with parameters $X$ affecting r... | |
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blog.ml.cmu.edu
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| | | | | The latest news and publications regarding machine learning, artificial intelligence or related, brought to you by the Machine Learning Blog, a spinoff of the Machine Learning Department at Carnegie Mellon University. | |
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francisbach.com
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