yangblin666/xiaoyang-machine-learning

★ 12HTMLAudience · dataComplexity · 1/5Setup · easy

Mindmap

mindmap
  root((xiaoyang-machine-learning))
    What It Covers
      ML basics
      Classical algorithms
      Deep learning
      Modern AI
    Modern Topics
      Transformers
      RAG
      AI agents
      LoRA fine-tuning
    Career Skills
      Research methods
      Interview prep
      Experiment design
    Audience
      University students
      AI enthusiasts

mindmap root((xiaoyang-machine-learning)) What It Covers ML basics Classical algorithms Deep learning Modern AI Modern Topics Transformers RAG AI agents LoRA fine-tuning Career Skills Research methods Interview prep Experiment design Audience University students AI enthusiasts

Click or tap to explore — scroll the page freely

Things people build with this

USE CASE 1

Study machine learning concepts from basics to modern AI using an organized, visual resource in Chinese

USE CASE 2

Prepare for job interviews at tech companies with algorithm questions and project presentation templates

USE CASE 3

Learn deep learning techniques including Transformers, RAG, and LoRA fine-tuning through structured explanations

USE CASE 4

Follow a three-pass reading method to build intuition before diving into formulas and research insights

Tech stack

HTML

Getting it running

Difficulty · easy Time to first run · 5min

No license information was provided in the explanation.

In plain English

xiaoyang-machine-learning is a Chinese-language educational website for learning machine learning and deep learning concepts. The entire site is contained in a single HTML file that opens directly in a browser without any installation, and is also hosted online for easy access. The site targets university students and AI enthusiasts at the beginning of their learning journey. It covers a progression from foundational concepts to modern techniques. The content starts with machine learning basics such as data, features, labels, models, loss functions, overfitting, and evaluation metrics, then moves to classical algorithms including linear regression, decision trees, random forests, gradient boosting variants (GBDT, XGBoost, LightGBM), SVM, and clustering methods. It continues into deep learning (neural networks, backpropagation, optimizers, batch normalization, and the PyTorch training workflow) and modern AI topics including Transformers, attention mechanisms, embeddings, retrieval-augmented generation, AI agents, LoRA fine-tuning, multimodal systems, and diffusion models. Beyond theory, the site includes sections on research skills: reading papers, designing experiments, running ablation studies, analyzing errors, and writing up results. There is also a section dedicated to preparing for job interviews at large technology companies, covering common algorithm interview questions, project presentation templates, and real business scenario analysis. The README suggests a three-pass reading approach: first for intuition and examples without stopping at formulas, second for formulas and code details, third for research insights and interview content. It encourages learners to ask themselves three questions about every algorithm they study: what it is, why it is still used, and what real problem it solves. The project is written in Chinese and intended for a Chinese-speaking academic audience. Contributions such as additional examples, paper notes, and interview questions are welcome.

Copy-paste prompts

Prompt 1

I am learning machine learning for the first time. Using the xiaoyang-machine-learning guide, explain gradient boosting and how GBDT, XGBoost, and LightGBM differ in plain language.

Prompt 2

Walk me through the PyTorch training workflow as described in the xiaoyang-machine-learning guide, including what each step does and why it matters.

Prompt 3

Using the research skills section from xiaoyang-machine-learning, help me design an ablation study for my NLP experiment and explain what I should measure.

Prompt 4

Based on the interview prep section in xiaoyang-machine-learning, give me 5 algorithm questions I should practice for a tech company AI interview.

Prompt 5

Using the xiaoyang-machine-learning three-pass reading approach, help me apply it to a new deep learning paper I just found.

Open on GitHub → Explain another repo

← yangblin666 on gitmyhub — every repo by this author, as a profile.

Verify against the repo before relying on details.