Machine Learning, Data Science, NLP, Mathematics, AI.
multiplying the gradient by the output of each successive layer. This means that the gradients of weights/biases that are present in the layers close to input can be smaller and smaller if the partial derivative terms it picks-up along its way less than 1. This phenomenon is called vanishing