In this article, I will explain Vanishing and Exploding Gradient Descent.
What is Gradient Descent?
Basically, Gradient Descent is a widely used optimization algorithm in machine learning to minimize the loss function during the training process of a neural network.
Vanishing Gradient
However, in deep neural networks, the gradients may become too small or too large, leading to slow convergence or even divergence. This problem is known as Vanishing or Exploding Gradient.
In fact, during backpropagation, the gradient signal flows from the output layer to the input layer through the weight matrices of each layer. In deep neural networks with many layers, the gradient signal can become very small, approaching zero. As a result, the earlier layers receive a very small signal. So, it results in very small weight updates. Therefore, the model takes a long time to converge. We call this problem Vanishing Gradient.
Exploding Gradient
Similarly, in some cases, the gradient signal can become very large, leading to an explosion in weight updates. As a result, the model becomes unstable. This problem is called Exploding Gradient.
The cause of these problems is the multiplication of gradients during backpropagation. So, as the signal passes through many layers, the gradient is multiplied repeatedly, leading to a very small or very large signal. Furthermore, the magnitude of the gradient depends on the weights of each layer, the activation function used, and the initialization of the weights.
As a matter of fact, several techniques have been developed to overcome the gradient problem. For instance, we can use different weight initialization methods, change the activation functions, and use gradient clipping. Another popular approach is to use normalization techniques such as Batch Normalization, Layer Normalization, and Group Normalization to normalize the inputs to each layer. Evidently, it reduces the effect of vanishing and exploding gradients.
Further Reading
Examples of OpenCV Library in Python
A Brief Introduction of Pandas Library in Python
A Brief Tutorial on NumPy in Python
Hyperparameters in an Artificial Neural Network (ANN)
Unleashing Creativity and Innovation with Drone Competitions in College
Breaking Boundaries: Innovative Project Ideas for Drones with Machine Learning
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