Pooling / Sub-sampling: Max, Mean - Revision history

BPeat at 01:57, 28 June 2019

2019-06-28T01:57:20Z

2019-06-28T01:56:47Z

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← Older revision		Revision as of 01:57, 28 June 2019
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	* [[(Deep) Convolutional Neural Network (DCNN/CNN)]]		* [[(Deep) Convolutional Neural Network (DCNN/CNN)]]

−	[http://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ Spatial Pooling (also called subsampling or downsampling) reduces the dimensionality of each feature map but retains the most important information. Spatial Pooling can be of different types: Max, Average, Sum etc. In case of Max Pooling, we define a spatial neighborhood (for example, a 2×2 window) and take the largest element from the rectified feature map within that window. Instead of taking the largest element we could also take the average (Average Pooling) or sum of all elements in that window. In practice, Max Pooling has been shown to work better. \| ujjwalkarn]	+	[http://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets/ Spatial Pooling (also called subsampling or downsampling)] reduces the dimensionality of each feature map but retains the most important information. Spatial Pooling can be of different types: Max, Average, Sum etc. In case of Max Pooling, we define a spatial neighborhood (for example, a 2×2 window) and take the largest element from the rectified feature map within that window. Instead of taking the largest element we could also take the average (Average Pooling) or sum of all elements in that window. In practice, Max Pooling has been shown to work better. \| ujjwalkarn

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 * [[Softmax]]
-* [[Dimensional Reduction Algorithms]]
+* [[Dimensional Reduction]] Algorithms
 * [[(Deep) Convolutional Neural Network (DCNN/CNN)]]

← Older revision		Revision as of 03:59, 3 February 2019
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		+	{{#seo:
		+	\|title=PRIMO.ai
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		+	\|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
		+	\|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
		+	}}
	[http://www.youtube.com/results?search_query=max+mean+pooling+sub-sampling+deep+learning YouTube search...]		[http://www.youtube.com/results?search_query=max+mean+pooling+sub-sampling+deep+learning YouTube search...]
		+	[http://www.google.com/search?q=max+mean+pooling+sub-sampling+deep+learning+deep+machine+learning+ML+artificial+intelligence ...Google search]

	* [[Softmax]]		* [[Softmax]]

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 * [[Dimensional Reduction Algorithms]]
 * [[(Deep) Convolutional Neural Network (DCNN/CNN)]]
 <youtube>8oOgPUO-TBY</youtube>

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 <youtube>I-JKxcpbRT4</youtube>
 <youtube>YFoOhmNZiSw</youtube>
-<youtube>I-JKxcpbRT4</youtube>
+<youtube>qzK7FSyZUxI</youtube>
 == Max Pooling ==
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 <youtube>ZjM_XQa5s6s</youtube>

← Older revision		Revision as of 16:58, 28 July 2018
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−	[http://www.youtube.com/results?search_query=pooling+deep+learning YouTube search...]	+	[http://www.youtube.com/results?search_query=max+mean+pooling+sub-sampling+deep+learning YouTube search...]

	* [[Softmax]]		* [[Softmax]]

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 [http://www.youtube.com/results?search_query=pooling+deep+learning YouTube search...]
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 == Max Pooling ==
 [http://www.youtube.com/results?search_query=max+Pool+maxpooling+Dimensional+Reduction+Algorithm Youtube search...]
 <youtube>ZjM_XQa5s6s</youtube>

← Older revision	Revision as of 16:45, 28 July 2018
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