vanishing gradients

• Weight Initialization: Starting Your Network Right

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  Weight initialization is a crucial step in setting up neural networks, as it can significantly impact the model's convergence and overall performance. Proper initialization helps avoid issues like vanishing or exploding gradients, which can hinder the learning process. Techniques such as Xavier and He initialization are commonly used to ensure weights are set in a way that maintains the scale of input signals throughout the network. Understanding and applying effective weight initialization strategies is essential for building robust and efficient deep learning models. This matters because it can dramatically improve the training efficiency and accuracy of neural networks.

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  Posted on

  Dec 30, 2025

  by

  TweakedGeekTech

  in

  Deep Dives, Learning

  Topics: Deep Learning, neural networks, model performance