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113+ Reproducible Experiments
Complete Jupyter Notebooks with detailed comments, mathematical derivations, and visualizations
AI-PracticesFull-Stack AI Learning Laboratory
A Systematic Approach to AI Research & Engineering
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Progressive Learning Framework
This project adopts the Progressive Learning Framework methodology, building a complete learning loop from theory to practice:
| Phase | Principle | Method | Output | Goal |
|---|---|---|---|---|
| Ⅰ | Theory First | Math derivation + Algorithm analysis | Theory notes | 🎯 Understand principles |
| Ⅱ | From Scratch | NumPy implementation from scratch | Core code | 🔧 Master details |
| Ⅲ | Framework | PyTorch / TensorFlow engineering | Production code | ⚡ Efficient development |
| Ⅳ | Practice | Kaggle competitions + Industry projects | Complete solutions | 🏆 Real-world skills |
Tech Stack
Quick Start
bash
# Clone repository
git clone https://github.com/zimingttkx/AI-Practices.git
cd AI-Practices
# Create Conda environment
conda create -n ai-practices python=3.10 -y
conda activate ai-practices
# Install dependencies
pip install -r requirements.txt
# Launch Jupyter Lab
jupyter labbash
# Clone repository
git clone https://github.com/zimingttkx/AI-Practices.git
cd AI-Practices
# Create virtual environment
python -m venv venv
source venv/bin/activate # Linux/macOS
# venv\Scripts\activate # Windows
# Install dependencies
pip install -r requirements.txt
# Launch Jupyter Lab
jupyter labLearning Roadmap
Start ──► 01 ML Foundations ──► 02 Neural Networks ──┬──► 03 Computer Vision ──┬──► 05 Advanced ──┬──► 06 Generative ──┐
│ │ │ │
└──► 04 Sequence Models ──┘ └──► 07 RL ─────────┼──► 09 Projects
│
08 Theory Notes ◄─────────────── Reference ──────────────────────┘Competition Results
| Competition | Rank | Medal | Year |
|---|---|---|---|
| Feedback Prize - ELL | Top 1% | 🥇 Gold | 2023 |
| RSNA Abdominal Trauma | Top 1% | 🥇 Gold | 2023 |
| American Express Default | Top 5% | 🥈 Silver | 2022 |
| RSNA Lumbar Spine | Top 10% | 🥉 Bronze | 2024 |