Understanding Weight Initialization in Neural Networks: Normal, Xavier, He, and Leaky He

Weight initialization is crucial in training neural networks, as it sets the starting point for optimization algorithms. The activation function applies a non-linear transformation in our network. Different activation functions serve different purposes. Choosing the right weight initialization and activation function is key to better neural network performance.

In this tutorial you'll learn:

* Gaussian/Normal initialization
* Xavier initialization for sigmoid/tanh
* He (Kaiming) initialization for ReLU
* Leaky He initialization
* Comparing different initialization methods
* Universal initialization implementation in Python

Code and resources:
https://github.com/nickovchinnikov/datasatanism/blob/master/code/8.WeightsAndActivation.ipynb

Full article:
https://datasanta.net/2025/01/14/weight-initialization-methods-in-neural-networks/

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Timestamps:

00:07 Introduction
01:03 Gaussian initialization
03:38 Xavier initialization
06:27 He (Kaiming) initialization
08:03 Leaky He initialization
09:33 Plot: Comparing Initialization Methods
11:28 Universal Parameter Implementation
17:28 Sign off

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Видео Understanding Weight Initialization in Neural Networks: Normal, Xavier, He, and Leaky He канала Data Santa