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Before proceeding, it's important to note that ResNets, as pointed out here, were not introduced to specifically solve the VGP, but to improve learning in general. In fact, the authors of ResNet, in the original paper, noticed that neural networks without residual connections don't learn as well as ResNets, although they are using batch normalization, which, ...


3

They explained in the paper why they introduce residual blocks. They argue that it's easier to learn residual functions $F(x) = H(x) - x$ and then add them to the original representation $x$ to get hidden representation $H(x) = F(x) + x$ than it is to learn hidden representation $H(x)$ directly from original representation. That's the main reason and ...


2

Vanishing Gradients can be detected from the kernel weights distribution. All you have to look for is whether the weights are dying down to 0. If only 25% of your kernel weights are changing that does not imply a vanishing gradient, it might be a factor, but there can be a variety of reasons, such as poor data, loss function used to the optimizer, etc. ...


2

Vanishing gradient is: as the gradient starts to flow from the end of the network (right side of the network) to the start of the network (left side of the network), it will be multiplied by numbers less than 1 and gradually it will become weaker and weaker and when it arrives to the first layers, it's so weak that makes almost no change in initial layers ...


2

There are several pitfalls or issues that require your attention when or before training or using neural networks. I will list some of them below, along with some questions you need to ask yourself before or while using neural networks. Over-fitting and under-fitting problems, and the related generalization problem. Is your neural network generalizing to ...


2

I can't say that it is the biggest problem of deep neural network but it is one of the big problem with deep neural network. Other issue which happens a lot is overfitting on the training data hence network behaves badly on test set which can be solved using regression. So you have to make sure that network is generalised enough. If the network is trying ...


1

It is an error. But it is also not the in final version of the paper (arxiv.) The final version of the paper can be found here where they replace "absolute value of the largest eigenvalue" with "largest singular value". We first prove that it is sufficient for $\lambda_1 < \frac{1}{\gamma}$, where $\lambda_1$ is the largest singular value of $W_{rec}$,...


1

"Naturally, this will affect the gradient through the network." this statement is only partially true, let's see why by starting explaining the real aim of batch normalisation. As the title of the paper suggest, the aim of batch normalisation is to decrease training time by reducing covariance shift. What is covariance shift? We can conceive it as the ...


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