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 LossGaussian 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 GELURMSNorm 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.
Read More »[Machine Learning] Note of RMSNorm
(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.
Read More »[Machine Learning] Note of Rotary Position Embedding (RoPE)
Scaled Dot-Product Attention (SDPA) might immediately pop into the minds of those familiar with the Transformer self-attention mechanism:
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
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.
Read More »[Paper Reading] Lifting the Curse of Multilinguality by Pre-training Modular Transformers
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.
Read More »Stable Diffusion ComfyUI Note 03 – How To Download SD Models
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.
Read More »Note Of Singular Value Decomposition (SVD)
The year 2023 witnessed an explosion of generative AI technologies, with a myriad of applications emerging across various domains. In the field of Natural Language Processing (NLP), Large Language Models (LLMs) stand out as one of the most significant advancements. By training LLMs effectively and reducing hallucinations, they can significantly reduce human effort across a wide range of tasks.
Read More »[Paper Reading] RAGAS: Automated Evaluation of Retrieval Augmented Generation
