(Deep) Residual Network (DRN) - ResNet - Revision history

BPeat: Text replacement - "http:" to "https:"

2023-03-28T00:49:35Z

Text replacement - "http:" to "https:"

BPeat at 04:16, 26 July 2020

2020-07-26T04:16:13Z

BPeat at 02:21, 15 August 2019

2019-08-15T02:21:56Z

BPeat at 12:09, 30 June 2019

2019-06-30T12:09:10Z

BPeat at 03:40, 3 February 2019

2019-02-03T03:40:55Z

BPeat at 03:03, 16 January 2019

2019-01-16T03:03:46Z

BPeat at 03:01, 16 January 2019

2019-01-16T03:01:43Z

BPeat at 02:59, 16 January 2019

2019-01-16T02:59:05Z

BPeat at 02:54, 16 January 2019

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BPeat at 02:46, 16 January 2019

2019-01-16T02:46:17Z

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=residual+neural+networks+ResNet YouTube search...]
+[https://www.youtube.com/results?search_query=residual+neural+networks+ResNet YouTube search...]
-[http://www.google.com/search?q=residual+neural+networks+ResNet+deep+machine+learning+ML ...Google search]
+[https://www.google.com/search?q=residual+neural+networks+ResNet+deep+machine+learning+ML ...Google search]
 * [[ResNet-50]]
@@ Line 14: / Line 14: @@
 * [[Protein Folding & Discovery]] ...[[Protein Folding & Discovery#Google DeepMind AlphaFold|Google DeepMind AlphaFold]]
 * [[Feed Forward Neural Network (FF or FFNN)]]
-* [http://arxiv.org/abs/1512.03385 Deep Residual Learning for Image Recognition | K. He, X. Zhang, S. Ren, and J. Sun]
+* [https://arxiv.org/abs/1512.03385 Deep Residual Learning for Image Recognition | K. He, X. Zhang, S. Ren, and J. Sun]
-* [http://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
+* [https://www.asimovinstitute.org/author/fjodorvanveen/ Neural Network Zoo | Fjodor Van Veen]
-Deep residual networks (DRN); called ResNets, are very deep Feed Forward Neural Networks (FFNNs) with extra connections; callled 'skip connections', passing input from one layer to a later layer (often 2 to 5 layers) as well as the next layer. Instead of trying to find a solution for mapping some input to some output across say 5 layers, the network is enforced to learn to map some input to some output + some input. Basically, it adds an identity to the solution, carrying the older input over and serving it freshly to a later layer. It has been shown that these networks are very effective at learning patterns up to 150 layers deep, much more than the regular 2 to 5 layers one could expect to train. However, it has been proven that these networks are in essence just Recurrent Neural Network (RNNs) without the explicit time based construction and they’re often compared to [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]] without gates. [http://arxiv.org/pdf/1512.03385.pdf Deep Residual Learning for Image Recognition | Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun @ Microsoft Research]
+Deep residual networks (DRN); called ResNets, are very deep Feed Forward Neural Networks (FFNNs) with extra connections; callled 'skip connections', passing input from one layer to a later layer (often 2 to 5 layers) as well as the next layer. Instead of trying to find a solution for mapping some input to some output across say 5 layers, the network is enforced to learn to map some input to some output + some input. Basically, it adds an identity to the solution, carrying the older input over and serving it freshly to a later layer. It has been shown that these networks are very effective at learning patterns up to 150 layers deep, much more than the regular 2 to 5 layers one could expect to train. However, it has been proven that these networks are in essence just Recurrent Neural Network (RNNs) without the explicit time based construction and they’re often compared to [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]] without gates. [https://arxiv.org/pdf/1512.03385.pdf Deep Residual Learning for Image Recognition | Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun @ Microsoft Research]
-http://i.stack.imgur.com/AdBoF.png
+https://i.stack.imgur.com/AdBoF.png
-http://www.asimovinstitute.org/wp-content/uploads/2016/09/drn.png
+https://www.asimovinstitute.org/wp-content/uploads/2016/09/drn.png

@@ Line 12: / Line 12: @@
 * [[Recurrent Neural Network (RNN)]]
 * [[Google DeepMind AlphaGo Zero]]
-* [[Google DeepMind AlphaFold]]
+* [[Protein Folding & Discovery]] ...[[Protein Folding & Discovery#Google DeepMind AlphaFold|Google DeepMind AlphaFold]]
 * [[Feed Forward Neural Network (FF or FFNN)]]
 * [http://arxiv.org/abs/1512.03385 Deep Residual Learning for Image Recognition | K. He, X. Zhang, S. Ren, and J. Sun]

← Older revision		Revision as of 02:21, 15 August 2019
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		+	<youtube>jio04YvgraU</youtube>
	<youtube>ZILIbUvp5lk</youtube>		<youtube>ZILIbUvp5lk</youtube>
	<youtube>Qoz1KCfPtBk</youtube>		<youtube>Qoz1KCfPtBk</youtube>

@@ Line 10: / Line 10: @@
 * [[ResNet-50]]
 * [[Deep Learning]]
-* [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]]
+* [[Recurrent Neural Network (RNN)]]
 * [[Google DeepMind AlphaGo Zero]]
 * [[Google DeepMind AlphaFold]]

← Older revision		Revision as of 03:40, 3 February 2019
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		+	{{#seo:
		+	\|title=PRIMO.ai
		+	\|titlemode=append
		+	\|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=residual+neural+networks+ResNet YouTube search...]		[http://www.youtube.com/results?search_query=residual+neural+networks+ResNet YouTube search...]
	[http://www.google.com/search?q=residual+neural+networks+ResNet+deep+machine+learning+ML ...Google search]		[http://www.google.com/search?q=residual+neural+networks+ResNet+deep+machine+learning+ML ...Google search]

← Older revision		Revision as of 03:03, 16 January 2019
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	<youtube>C6tLw-rPQ2o</youtube>		<youtube>C6tLw-rPQ2o</youtube>
	<youtube>XPpmzulEmjA</youtube>		<youtube>XPpmzulEmjA</youtube>
		+	<youtube>jFJF5hXuo0s</youtube>

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 <youtube>ZILIbUvp5lk</youtube>
 <youtube>0tBPSxioIZE</youtube>
 <youtube>9w13O3gwUOQ</youtube>
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 <youtube>e6hr9YVu0RI</youtube>
 <youtube>C6tLw-rPQ2o</youtube>

@@ Line 1: / Line 1: @@
-[[http://www.youtube.com/results?search_query=residual+neural+networks+ResNet YouTube search...]]
+[http://www.youtube.com/results?search_query=residual+neural+networks+ResNet YouTube search...]
 * [[ResNet-50]]
@@ Line 13: / Line 14: @@
 http://www.asimovinstitute.org/wp-content/uploads/2016/09/drn.png
-Deep residual networks (DRN); called ResNets, are very deep Feed Forward Neural Networks (FFNNs) with extra connections; callled 'skip connections', passing input from one layer to a later layer (often 2 to 5 layers) as well as the next layer. Instead of trying to find a solution for mapping some input to some output across say 5 layers, the network is enforced to learn to map some input to some output + some input. Basically, it adds an identity to the solution, carrying the older input over and serving it freshly to a later layer. It has been shown that these networks are very effective at learning patterns up to 150 layers deep, much more than the regular 2 to 5 layers one could expect to train. However, it has been proven that these networks are in essence just Recurrent Neural Network (RNNs) without the explicit time based construction and they’re often compared to Long Short-Term Memory (LSTMs) without gates. [http://arxiv.org/pdf/1512.03385.pdf Deep Residual Learning for Image Recognition | Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun @ Microsoft Research]
+Deep residual networks (DRN); called ResNets, are very deep Feed Forward Neural Networks (FFNNs) with extra connections; callled 'skip connections', passing input from one layer to a later layer (often 2 to 5 layers) as well as the next layer. Instead of trying to find a solution for mapping some input to some output across say 5 layers, the network is enforced to learn to map some input to some output + some input. Basically, it adds an identity to the solution, carrying the older input over and serving it freshly to a later layer. It has been shown that these networks are very effective at learning patterns up to 150 layers deep, much more than the regular 2 to 5 layers one could expect to train. However, it has been proven that these networks are in essence just Recurrent Neural Network (RNNs) without the explicit time based construction and they’re often compared to [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]] without gates. [http://arxiv.org/pdf/1512.03385.pdf Deep Residual Learning for Image Recognition | Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun @ Microsoft Research]
-<youtube>K0uoBKBQ1gA</youtube>
+Skip connections:
-<youtube>GSsKdtoatm8</youtube>
+Residual block:
 <youtube>9w13O3gwUOQ</youtube>
 <youtube>hwMsKmgopSU</youtube>
 <youtube>UlnYEWXoxOY</youtube>
 <youtube>e6hr9YVu0RI</youtube>