Uploaded July 2024 | Updated September 2026, 1 week ago
Sebastian's books: sebastianraschka.com/books
This video explains what PyTorch buffers are, a concept that is particularly useful when dealing with GPU computations and implement large models like LLMs.
Code notebook: github.com/rasbt/LLMs-from-scratch/blob/main/ch03/03_understanding-buffers/understanding-buffers.ipynb
GitHub discussion about "triu" in the forward pass: github.com/rasbt/LLMs-from-scratch/discussions/282
Link to the Studio GPU environment to follow along: lightning.ai/seraschka/studios/understanding-pytorch-buffers?section=recent
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To support this channel, please consider purchasing a copy of my books: sebastianraschka.com/books
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twitter.com/rasbt
linkedin.com/in/sebastianraschka
magazine.sebastianraschka.com
Sebastian's books: sebastianraschka.com/books
This video explains what PyTorch buffers are, a concept that is particularly useful when dealing with GPU computations and implement large models like LLMs.
Code notebook: github.com/rasbt/LLMs-from-scratch/blob/main/ch03/03_understanding-buffers/understanding-buffers.ipynb
GitHub discussion about "triu" in the forward pass: github.com/rasbt/LLMs-from-scratch/discussions/282
Link to the Studio GPU environment to follow along: lightning.ai/seraschka/studios/understanding-pytorch-buffers?section=recent
---
To support this channel, please consider purchasing a copy of my books: sebastianraschka.com/books
---
twitter.com/rasbt
linkedin.com/in/sebastianraschka
magazine.sebastianraschka.com




![L11.6 Xavier Glorot and Kaiming He Initialization
Sebastians books: https://sebastianraschka.com/books/I
MPORTANT NOTE: In the video, I talk about the number of input units in the denominator (fan in), but to be correct, it should have been number of input units for both the current and the next layer (fan in + fan out).
Slides: https://sebastianraschka.com/pdf/lecture-notes/stat453ss21/L11_norm-and-init slides.pdf
Papers:
Xavier Glorot and Yoshua Bengio. Understanding the difficulty of training deep feedforward neural networks. Proceedings of the thirteenth international conference on artificial intelligence and statistics. 2010. http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf?hc_location=ufi]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE international conference on computer vision, pp. 1026-1034. 2015. https://arxiv.org/abs/1502.01852
This video is part of my Introduction of Deep Learning course.
Next video: https://youtu.be/nA6oEAE9IVc
The complete playlist: https://www.youtube.com/playlist?list=PLTKMiZHVd_2KJtIXOW0zFhFfBaJJilH51
A handy overview page with links to the materials: https://sebastianraschka.com/blog/2021/dl-course.html
If you want to be notified about future videos, please consider subscribing to my channel: https://youtube.com/c/SebastianRaschka L11.6 Xavier Glorot and Kaiming He Initialization](https://i.ytimg.com/vi/ScWTYHQra5E/mqdefault.jpg)





