In-Depth Look: ResNet Architecture and Residual Block Explained

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In this video 📝 we will talk about the ResNet Architecture. Residual Neural Networks are often used to solve computer vision problems and consist of several residual blocks. We will talk about what a residual block is and compare it to the architecture of a standard convolutional neural network. I'll show you how you can use the pre-trained ResNets from Keras and TensorFlow.

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Tags:
#ResNet #ResidualBlock #NeuralNetworks #DeepLearning #NeuralNetworksPython #NeuralNetworksTutorial #DeepLearningTutorial #Keras #TensorFlow

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NicolaiAI

You clearly know what you're talking about, but your presentation's a mess - you should write, and stick to, a script.
Don't be afraid to take a breath, it's like you're rushing to get your sentences out.

Nagrom

Thanks for the video. I think I finally understand why they are so effective. I'd love to look at a simple trained resnet so I can analyze the weights and see what's happening

mfpears

I love the way he explain this architecture, helps me a lot to understand ResNet.

huangshijie

Helped me a lot. Keep on making great stuff like this!!

itsfabiolous

here counter - 161 times in 13 minutes
12 here/minute
which turns to 1 here every 5th second.

Graveness

Very good explanation. It will help me in research.

jagyansenipandapanda

Has the resnet-1000 architecture been built?

PE-gwgu

Totally don't understand why it's hard to compute the direct mapping in the standard CNN, or more like, why it's easy to do it with residual layers - in the residual layer, in order to get f(X) + X, you still have to compute f(X), right?
Furthermore, what do you mean by direct mapping? Is it just f(X)?

petarulev

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