The term Gradient Vanishing Problem originates from the fields of artificial intelligence and digital transformation. It describes a challenge in training so-called artificial neural networks, which form the basis of modern AI applications.
When a neural network learns, it gradually adjusts its „weights“ using a mathematical method to deliver ever-improving results. This happens in many small steps that run through all layers of the network from front to back. The vanishing gradient problem occurs when these adjustment steps at the very beginning of the network become so small that they almost disappear. As a result, the network „forgets“ almost everything that happens in its initial layers, and training stalls or becomes impossible.
A simple example: imagine you want to inform a long line of people by shouting. If each person barely passes on what they heard, nothing will reach the end.
The vanishing gradient problem is particularly relevant for very deep neural networks, i.e. those with many layers. Solutions such as special building blocks (for example, „LSTM“ cells in AI for language) help to circumvent this problem.
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