A Simple Explanation of Layer Normalization in Neural Networks

This video provides a simple and straightforward explanation of what layer normalization is and how it works in neural networks. Layer normalization is a technique used to speed up the training of deep neural networks by normalizing the inputs to each layer. We will cover:

- What normalization means and why it is important for training neural networks
- How layer normalization works by normalizing the inputs for each layer across ... samples in a batch
- When to use layer normalization versus other techniques like batch normalization
- The pros and cons of layer normalization

Even if you are new to deep learning, by the end of this video you'll have a solid understanding of what layer normalization is and how it can help improve the performance of your neural networks.