14. Advanced Image Classiers - ImageNet in Keras (VGG1619, InceptionV3, ResNet50)/4. Understanding ResNet50.srt

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Hi and welcome to chapter fourteen point three where we talk about resonate 50 arrests 50 was the winner

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of the Eilis our AVC competition in 2015.

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It was developed by district researchers here and present resin that stands for residual network.

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And that's because it uses the concept of residual linning which helps it in a number of ways.

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And I'll talk about those ways now.

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So the beauty of Reznor was that it got around a problem of just making instead of making networks deeper

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and deeper which was the trend back in 2015.

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And by doing that by making it deeper and deeper People were coming up with coming across accuracy becoming

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saturated and integrating rapidly during training which was not good.

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So what resonated was that it introduced a shallow architecture with a deep deep residual learning framework

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and resonate instead of leaving high mid and low mid and high level features.

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It lends residuals by using short cut connections directly connecting the input of the player to an

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end.

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Plus ex-slave.

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So this results in far easier training and resolves to degrading accuracy problem.

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This is what the resonant residual module looks like here.

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Can Take a look.

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Basically this is a x one layer.

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Here goes through this all these layers here and added back to the experts.

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One layer here.

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So now let's move on to Inception vision tree.