[Machine Learning] Note of LayerNorm
Last Updated on 2024-08-19 by Clay
The working principle of LayerNorm is as follows:
- Calculate mean and variance
[Machine Learning] Note of LayerNorm
[Machine Learning] Note of Cross Entropy Loss
Last Updated on 2024-08-18 by Clay
Introduction to Cross Entropy
Cross entropy is a very common loss function in Machine Learning, as it is able to quantify the difference between a model's classification predictions and the actual class labels, particularly in 'classification tasks'.
Read More »[Machine Learning] Note of Cross Entropy Loss[Machine Learning] Note of Activation Function GELU
Last Updated on 2024-08-18 by Clay
Gaussian Error Linear Unit (GELU) is an activation function used in machine learning. While it resembles the classic ReLU (Rectified Linear Unit), there are some key differences.
Read More »[Machine Learning] Note of Activation Function GELU[Machine Learning] Note of RMSNorm
Last Updated on 2024-08-17 by Clay
Introduction to RMSNorm
RMSNorm is an improvement over LayerNorm, often used in the Transformer self-attention mechanism. It aims to mitigate the issues of vanishing and exploding gradients, helping the model converge faster and improve performance.
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[Machine Learning] Note of Rotary Position Embedding (RoPE)
Last Updated on 2024-08-17 by Clay
Introduction
(Note: Since this article is imported from my personal Hackmd, some symbols and formatting might not display properly in WordPress. I appreciate your understanding, sorry for any inconvenience.)
RoPE is a method for introducing relative position information into the self-attention mechanism through absolute positional encoding.
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[PyTorch] Using SDPA in 2.0+ to Improve the Computation Speed of Transformer’s Self-Attention Mechanism
Last Updated on 2024-08-16 by Clay
SDPA Introduction
Scaled Dot-Product Attention (SDPA) might immediately pop into the minds of those familiar with the Transformer self-attention mechanism:
[Linux] tty-clock: A Tool For Displaying Electronic Clock Time On Terminal
Last Updated on 2024-08-14 by Clay
Introduction
I often use my computer to watch full-screen videos and need to promptly return to work mode when the time comes — but embarrassingly, I don't like constantly picking up my phone to check the current time, as it's quite inconvenient.
Read More »[Linux] tty-clock: A Tool For Displaying Electronic Clock Time On Terminal
[Paper Reading] Lifting the Curse of Multilinguality by Pre-training Modular Transformers
Last Updated on 2024-08-19 by Clay
Cross-lingual Modular (X-Mod) is an interesting language model architecture that modularizes the parameters for different languages as Module Units, allowing the model to use separate parameters when fine-tuning for a new language, thereby (comparatively) avoiding the problem of catastrophic forgetting.
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Stable Diffusion ComfyUI Note 03 - How To Download SD Models
Last Updated on 2024-08-12 by Clay
When using ComfyUI to generate images, we need to leverage the capabilities of various models to ultimately form a complete workflow. In other words, these so-called various models together constitute what we call Stable Diffusion. Today, I will introduce where to download these models.
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Note Of Singular Value Decomposition (SVD)
Last Updated on 2024-08-11 by Clay
Singular Value Decomposition (SVD) is a method for decomposing a matrix into three matrices, revealing the rank, data dimensions, and key directions of the original matrix. It is often used in dimensionality reduction, compression, and structural analysis.
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