[Artificial Intelligence] The essence of large models: highly compressed mapping of all human knowledge in ultra-high dimensional space
Article directory
- [Artificial Intelligence] The essence of large models: highly compressed mapping of all human knowledge in ultra-high dimensional space
- Chapter 1 Introduction
- Chapter 2 Definition of Large Model
- Chapter 3 The Nature of Large Models
- Chapter 4 Advantages of Large Models
- Chapter 5 Challenges of Large Models
- Chapter 6 Application of large models
- Chapter 7 Large Model Training Technology
- Chapter 8 Evaluation Criteria for Large Models
- Chapter 9 Future Development Trends of Large Models
- Chapter 10 Summary
Chapter 1 Introduction
In the fields of computer science and artificial intelligence, large models have become one of the hot topics of current research. Large models usually refer to deep neural network models with hundreds of millions of parameters. In recent years, the emergence of giant natural language processing models such as GPT-3 has attracted widespread attention and discussion. This article will introduce in detail the nature and application of large models from both theoretical and practical perspectives.
Chapter 2 Definition of Large Model
Large models refer to deep neural network models with more than 10 million parameters. Currently, large models are mainly used in fields such as natural language processing, image recognition, and recommendation systems. Large models often use very complex structures and algorithms to extract the most effective features from massive amounts of data.
Chapter 3 The Nature of Large Models
The essence of the large model is to store all human knowledge in an ultra-high-dimensional space.