update251111-1

Author: issyugk3-zxw

docs/.vuepress/navbar.ts

@@ -24,6 +24,8 @@ export const enNavbar = defineNavbarConfig([
     icon:'material-symbols:stylus-note-outline-rounded',
     items: [
       { text: 'Machine Learning', link: '/ML/',icon: 'carbon:ibm-machine-learning-for-zos' },
+      { text: "Bioinformatics", link: '/Bioinformatics/',icon: 'mdi:dna' },
+      { text: "Deep Learning", link: '/DeepLearning/',icon: 'mdi:robot' },
 
     ]
   },

docs/.vuepress/plume.config.ts

@@ -16,7 +16,7 @@ export default defineThemeConfig({
 
   social: [
     { icon: 'github', link: 'https://github.com/issyugk3-zxw' },
-    { icon: 'x', link:'https://x.com/issyugk3'},
+    { icon: 'x', link:'https://x.com/issyugk3-zxw'},
     { icon: 'linkedin', link:''},
     { icon: {svg:kaggle_svg, name:'kaggle'}, link:'https://kaggle.com/issyugk3'},
 

docs/notes/Bioinformatics/1.Some Concepts/Common Concepts.md

@@ -0,0 +1,6 @@
+---
+title: Common Concepts
+createTime: 2025/10/08 15:51:33
+permalink: /Bioinformatics/yt6cyem3/
+---
+## Genome

docs/notes/Bioinformatics/README.md

@@ -0,0 +1,12 @@
+---
+title: Bioinformatics
+createTime: 2025/10/07 15:45:35
+permalink: /Bioinformatics/
+sidebar: 'auto'
+---
+This section is mainly about notes on Bioinformatics. Bioinformatics is an amazing area based on biology&medical science, statistics, machine learning and now, deep learning.
+
+It has many concepts for me to take before I get on research. So I decided to start this section to mark down some important things. Hope it could help you too. Please check.
+## Basic of Bioinformatics
+- [1.Common Concepts](/Bioinformatics/yt6cyem3/)
+

docs/notes/ML/0.Recommend-Paper/1.Deep Generative Models.md

@@ -0,0 +1,12 @@
+---
+title: Deep Generative Models
+createTime: 2025/11/11 12:01:53
+permalink: /ML/xgngs9lh/
+---
+# Papers for deep generative models
+
+## Unsupervised Learning of Disentanglement
+
+- [β-VAE: LEARNING BASIC VISUAL CONCEPTS WITH A
+CONSTRAINED VARIATIONAL FRAMEWORK](https://www.cs.toronto.edu/~bonner/courses/2022s/csc2547/papers/generative/disentangled-representations/beta-vae,-higgins,-iclr2017.pdf)
+- [Challenging Common Assumptions in the Unsupervised Learning of  Disentangled Representations](https://arxiv.org/abs/1811.12359)

docs/notes/ML/0.Recommend-Paper/README.md

@@ -0,0 +1,8 @@
+---
+title: README
+createTime: 2025/11/11 12:00:13
+permalink: /ML/4pveufqm/
+---
+This is a collection of papers that I recommended.
+
+- [Deep Generative Models](/ML/xgngs9lh/)

docs/notes/ML/2.Frequentist-Models/1.Binary-Distributions.md

@@ -13,6 +13,7 @@ I would be grateful if anyone read this could offer me some advice.
 Here are the links to the notes:
 
 ## Traditional Learning
+- [0.Recommended Papers](/ML/4pveufqm/)
 - [1.Bayeian-view Methods](/ML/u0q5o2j0/)
 - [2.Frequentist-view Methods](/ML/sa39a9gm/)
 ## Modern Deep Learning