---
# System prepended metadata

title: 關於我
tags: [data science, LDA, "\_Locally linear embedding", Diffusion maps, "\_LLE", LORA, PCA, K-medoids, Power Method, Frobenius–Perron, NLP, K-means, "\_LE", Isometric mapping, Data Analysis, Linear algebra, "\_Kernel PCA", Non-linear dimension reduction, Laplacian eigenmap, Eigenvalues and eigenvectors, Isomap, Diffusion map, geodesic distance, LLM, KNN, LoRA, Principal Component Analysis, positive definite matrices, MDS, Linear Discriminant Analysis, Linear dimension reduction, TILT, SVD, Clustering problem, Stable diffusion, RPCA, classification problem, Singular Value Decomposition, computer vision]

---

# Larry’s Data Science & AI Notes

> A curated collection of my notes and tutorials on **Data Science**, **Machine Learning**, **Computer Vision**, and **Backend Deployment**.  
> 持續更新中

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## 👤 About Me
- [About Me](https://hackmd.io/@xhw14616/aboutme)

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## Fundamentals of Data Science  

### Dimensionality Reduction
- [Dimensionality Reduction: Overview & Motivation](https://hackmd.io/@xhw14616/Hk7JAX0pJe)
- [PCA](https://hackmd.io/@xhw14616/PCA)
- [LDA](https://hackmd.io/@xhw14616/LDA)
- [MDS](https://hackmd.io/@xhw14616/MDS)
- [Isomap](https://hackmd.io/@xhw14616/Isomap)
- [Diffusion Maps](https://hackmd.io/@xhw14616/diffusionmaps)
- [Laplacian Eigenmaps](https://hackmd.io/@xhw14616/LE)
- [LLE](https://hackmd.io/@xhw14616/LLE)
- [Kernel PCA](https://hackmd.io/@xhw14616/PCA)

👉 如果你覺得基礎忘光了：
- [A Linear Algebra Refresher](/lWjCJiLDRUOzuv4wnHPysQ)

### Clustering
- [Clustering: k-means and k-medoids](https://hackmd.io/@xhw14616/clustering)

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## Computer Vision
- [Low Rank Representation, RPCA and TILT](https://hackmd.io/@xhw14616/Bk1QtTApJx)
- [Image-to-Image](https://hackmd.io/@S8Nc-7uTRbWbcCkMYNvWXA/S1OYcystxx)

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## Training Techniques
- [LoRA: Low-Rank Adaptation](https://hackmd.io/@xhw14616/lora)

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## Backend & Deployment
- [Kubernetes architecture](https://hackmd.io/@xhw14616/k8s_ar)
- [Deploying a Service to GKE](https://hackmd.io/@xhw14616/k8s)