[CUDA] CPU vs GPU with python
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This column compares the speed of the CPU and GPU.
Posts by Collection
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[DS]Data Preprocessing - UnderSampling and OverSampling
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This time we will look at the basic concepts of UnderSampling and OverSampling.
[DS]Data Preprocessing - How Does Normalization Affect Random Forest?
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This time, we will look at how normalization affects the Random Forest technique. To understand this accurately, we need to understand the concepts of Random Forest and Normalization.
[DS]Data Preprocessing - Choosing Between One-Hot Encoding and Label Encoding in Random Forest
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Which should you choose between One-Hot Encoding and Label Encoding in Random Forest?
[DS] Optimization Models - Hyperparameter Tuning with Bayesian Approach
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Let’s try to understand Bayesian optimization from the basic perspective of the Bayesian approach.
paper_review
Attention Is All You Need
Published in , 2024
1. Introduction
Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations
Published in , 2024
Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Published in , 2024
Summarization of GPTQ
FLEXTRON: Many-in-One Flexible LargeLanguage Model
Published in , 2024
Today, I will summarize the paper titled “FLEXTRON: Many-in-One Flexible Large Language Model.” The primary focus of this paper is to propose a novel framework with an elastic structure that can quickly adapt to diverse user environments. To achieve this, paper suggests that like Mixture-of-Experts.
Matryoshka Quantization
Published in , 2025
In the era of massive language models and vision transformers, model efficiency has become just as important as accuracy. Whether you’re deploying on mobile, edge devices, or scaling inference infrastructure, quantization is a crucial technique for compressing models while maintaining performance.