Introduction Xavier initialization, also known as Glorot initialization, is a crucial technique in the field of deep learning for initializing the weights of neural networks. Proposed by Xavier Glorot and Yoshua Bengio in their 2010 paper “Understanding the difficulty of training deep feedforward neural networks,” this method has become a cornerstone in training deep neural … Continue reading Xavier/Glorot Initialization: Advanced Techniques and Implementation in Deep Learning