U8-02 V1 Wie LLMs funktionieren V3-
Updated: September 11, 2025
Summary
The video covered the main types of generative AI applications including text, audio, code, and visual content. It introduced popular language models like GPT, Coher, Bard, and Bing which are trained on massive text data to effectively model natural languages. The foundation models are trained through self-supervised learning on large text data, enhancing language knowledge and performance in natural language generation. The key elements of large language models like GPT3 were explained, including training data, token conversion, learning algorithm, probability distribution, and decoding algorithm for generating smooth and logical natural text. The tokenization process, which involves splitting text into tokens, assigning unique IDs, and transforming text into machine-readable vector representations for easier processing and analysis, was also detailed.
Types of Generative AI Applications
Covered the main types of generative AI applications like text, audio, code, and visual content.
Foundation Models like GPT, Coher, Bard, and Bing
Introduction to popular language models like GPT, Coher, Bard, and Bing trained on massive text data with a large number of parameters to model natural languages effectively.
Foundation Models Training and Pre-training
Foundation models are trained through self-supervised learning on large text data and pre-trained for various tasks, enhancing language knowledge and performance in natural language generation.
Fundamental Elements of LLMs like GPT3
Explained the key elements of large language models like GPT3 including training data, token conversion, learning algorithm, probability distribution, and decoding algorithm to generate smooth and logical natural text.
Tokenization Process
Detailed the tokenization process of splitting text into tokens, assigning unique IDs, and transforming text into machine-readable, vector representations for easier processing and analysis.
FAQ
Q: What are some main types of generative AI applications mentioned in the file?
A: Text, audio, code, and visual content.
Q: Can you name popular language models discussed in the file?
A: GPT, Coher, Bard, and Bing.
Q: How are foundation models trained in the context of language models?
A: They are trained through self-supervised learning on large text data and pre-trained for various tasks.
Q: What are the key elements of large language models like GPT3?
A: Training data, token conversion, learning algorithm, probability distribution, and decoding algorithm.
Q: What is the tokenization process in the context of language models?
A: It is the process of splitting text into tokens, assigning unique IDs, and transforming text into machine-readable, vector representations.
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