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What is the problem of vanishing gradients in neural networks?
This problem makes it hard to learn and tune the parameters of the earlier layers in the network. The vanishing gradients problem is one example of unstable behaviour that you may encounter when training a deep neural network.
How is the vanishing gradient problem used in machine learning?
In Machine Learning, the Vanishing Gradient Problem is encountered while training Neural Networks with gradient-based methods (example, Back Propagation). This problem makes it hard to learn and tune the parameters of the earlier layers in the network.
Who is the creator of the vanishing gradient problem?
The vanishing gradient problem was discovered by Sepp Hochreiter, a German computer scientist who has had an influential role in the development of recurrent neural networks in deep learning. Now let’s explore the vanishing gradient problem in detail.
Why do the gradients even vanish / explode?
Why do the gradients even vanish/explode? Certain activation functions, like the logistic function (sigmoid), have a very huge difference between the variance of their inputs and the outputs. In simpler words, they shrink and transform a larger input space into a smaller output space that lies between the range of [0,1].
How does ResNet solve the vanishing gradient problem?
The ResNet architecture, shown below, should now make perfect sense as to how it would not allow the vanishing gradient problem to occur. ResNet stands for Residual Network. These skip connections act as gradient superhighways, allowing the gradient to flow unhindered.
How does batch normalization solve the vanishing gradient problem?
As stated before, the problem arises when a large input space is mapped to a small one, causing the derivatives to disappear. In Image 1, this is most clearly seen at when |x| is big. Batch normalization reduces this problem by simply normalizing the input so |x| doesn’t reach the outer edges of the sigmoid function.