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XiDianJiale/README.md

👋 Hi, I'm Jiale Hao

🎓 Undergraduate at Xidian University 🎯 Aiming for Backend → SRE career 💡 Passionate about backend systems, containerization, and cloud-native Bestpractice. 🐧 Currently exploring Go, Docker, and infrastructure automation.


🛠️ Tech Stack

Languages: 🐍 Python · ☕ Java · 🐹 Go DevOps & Tools: 🐳 Docker · ⚙️ GitHub Actions · 🌐 Nginx · 🔧 Git · 🎨 Streamlit Concepts: CI/CD · Containerization · REST APIs · Reinforcement Learning (DQN) · Computer Vision (ResNet, ViT, Transformer-MSA)


💼 Featured Projects

Python/Jupyter implementations of classic RL algorithms (e.g., DQN on CartPole) and model experiments (ResNet/ViT/Transformer-MSA). 📊 Focus: Reproducibility & clear architecture.


Interactive Streamlit app simulating currency management and AI assistant. 🧩 Role-based pages (User/Admin), product tabs, and a chatbot module. 💻 Quick start:

pip install -r requirements.txt
streamlit run money_classifier.py

Minimal CI/containers practice project. 🚀 Includes Dockerfile, Nginx config, and GitHub Actions workflow. 💬 Good showcase for container deployment basics.


Lightweight Go-based instant messaging service skeleton. ⚡ Simple architecture with main.go, server.go, and user.go. 📖 Includes personal learning notes.


Java Gradle project exploring authentication and billing logic. 🧾 Includes config (application.yml) and reflective documentation.


🎓 Education

Xidian University — Bachelor’s degree (in progress) 📆 2023–Present · Major: [Computer Science / Artificial Intelligence]


🌐 Languages

🇨🇳 Chinese (Native) · 🇬🇧 English (C1) · 🇩🇪 German (A1)


📫 Contact

📧 23009101432@stu.xidian.edu.cn 💼 GitHub · [LinkedIn]


Pinned Loading

  1. auth-billing auth-billing Public

    Java

  2. DeepReinforcementL-Algorithm DeepReinforcementL-Algorithm Public

    make mainstream DRL algorithm accessible and atomic

    Jupyter Notebook

  3. streamlit streamlit Public

    Python

  4. IM-System IM-System Public

    Go

  5. octo-models/octo octo-models/octo Public

    Octo is a transformer-based robot policy trained on a diverse mix of 800k robot trajectories.

    Python 1.8k 282