As of May 31, 2023, we have updated our Code of Conduct.

For questions related to the exploding gradient problem, which is the numerical problem associated with the significant increase (or explosion) of the numbers of the gradient vector of an objective function with respect to the parameters of a neural network, which is being trained with a gradient-based optimization algorithm and backpropagation. There is also the related vanishing gradient problem, which arises when the numbers become very small.

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### What is the justification for this approach of clipping elementwise?

I'm new to the field of AI (though I have a background in mathematics). As I was going through some documents, I read that there is a form of gradient clipping where the elements of the gradient that ...
1 vote
20 views

### Can an activation function with large derivative cause exploding gradient?

The maximum derivative of most of the currently existing activation functions is around 1. Can an activation function with derivatives higher than 1, say 1000 (a), cause exploding gradient problem? ...
1 vote
183 views

### Mathematically speaking, Is it only the product operation used in the chain rule causing the vanishing or exploding gradient?

I am asking this question from the mathematical perspective of the vanishing and exploding gradient problems that we face generally during training deep neural networks. The chain rule of ...
2k views

### How to deal with large (or NaN) neural network's weights?

My weights go from being between 0 and 1 at initialization to exploding into the tens of thousands in the next iteration. In the 3rd iteration, they become so large that only arrays of nan values are ...
9k views

### Why do ResNets avoid the vanishing gradient problem?

I read that, if we use the sigmoid or hyperbolic tangent activation functions in deep neural networks, we can have some problems with the vanishing of the gradient, and this is visible by the shapes ...
147 views

### What are the common pitfalls that we could face when training neural networks?

Apart from the vanishing or exploding gradient problems, what are other problems or pitfalls that we could face when training neural networks?