Maybe you have not used it and listened to its legend.

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In any case, I understand a little bit, but it is difficult to explain clearly, how is it generated? Next, we will explain the principles of its generation and understand four large-model technical architectures for building AI applications. 1. The generation principle of the large model First of all, what we need to understand is that the GPT large model is a natural language processing model based on deep learning, which is LLM. Knocking on the blackboard, LLM is a model for generating text, such as DALL·E. It and LLM are both branches of the multi-modal language model. Its working principle can be simply understood as "the law of learning language",


and its generation The method is just to guess the probability of the next word based on the above. So why does it have so much knowledge? That's because during the model training process, the GPT model will read a large amount Argentina WhatsApp Number text data and then learn the language patterns in these texts. This process can be compared to the way humans learn language. When we are babies, we learn the patterns of language by listening to our parents and those around us. How to define B-end products and B-end product manager methodology Compared with C-end products, the biggest feature of B-end products is that they are oriented to users in specific fields, and the number is much smaller,





but they pay more attention to the in-depth exploration of the operating processes in users’ professional fields—— That is to say, it is more professional and more closely integrated with the business. View details > For example, we will learn that "I" is usually followed by "yes", "you" is usually followed by "ok" and so on. This is a language rule. The GPT model learns the rules of language in a similar way. However, the learning ability of the GPT model far exceeds that of humans. It can read billions of texts and learn very complex language patterns. This is why the GPT model can generate very natural and coherent text. 2. The rules of how the GPT model learns language At this point, we need to understand the internal structure of the GPT model.