A Practical Summary of AI Technology Exploration

Background

2023 was a year of rapid progress in artificial intelligence, with large models and AI applications emerging one after another. To keep pace, I began exploring AI technology in depth at work, covering deep learning frameworks, large language models, and AI application development.

Key Technical Points

1. Deep Learning Frameworks

Studied the PyTorch framework in depth, grasping core concepts such as tensor computation, automatic differentiation, and building neural networks.

2. Large Language Models

Studied the architecture and application of large language models such as ChatGLM, and learned the basics of fine-tuning and deploying models.

3. Application Development Frameworks

Learned the LangChain framework and how to combine large language models with real-world applications.

4. Computer Vision

Explored image generation technologies such as Stable Diffusion, and practiced applications of image processing and generation.

Hands-On Projects

During my AI exploration, I took part in the following hands-on projects:

1. Research and technology selection for an AI customer service system
2. Development of AI video tools (image-to-text, video enhancement)
3. Research and applied practice with Stable Diffusion models

Key Takeaways

Through this deep exploration and hands-on practice with AI, I not only gained the relevant technical knowledge but, more importantly, came to understand where AI technology creates value in real business scenarios. This laid the foundation for my further development in the AI field.