This course is designed for students with prior programming experience. It covers essential Python concepts and data processing techniques, and guides students in building deep learning neural network models (ANN, CNN, RNN/LSTM) widely used in computer vision and natural language processing tasks.

The course also introduces core AI 2.0 technologies, including the Transformer architecture (BERT/GPT), multimodal learning, reinforcement learning (RL), retrieval-augmented generation (RAG), and autonomous agent systems.

Project topics are carefully selected from high-level sources such as MIT, Stanford, Kaggle, and Nature. Students will complete integrated development projects involving AI servers and mobile control panels, strengthening their capabilities in algorithm implementation and system deployment.

Students will use a variety of modern AI and software engineering tools throughout the course, including:

• Python
• Jupyter Notebook / Google Colab
• VS Code
• Git and GitHub
• Replit
• Claude
• OpenAI APIs
• Hugging Face
• Flask / FastAPI
• React.js
• Node.js
• SQL / NoSQL databases
• AWS cloud services