What is AI big model training and application reasoning
The training and application reasoning of AI large model are two important concepts in the field of artificial intelligence.
Training refers to the use of optimization algorithms to adjust the parameters of the model through a given data set, so that the model can better fit the data. In this process, the model will continuously propagate forward and backward according to the input data, so as to update the parameters to reduce the value of the loss function. This process requires a lot of computing resources and time, and usually requires the use of high-performance computing devices such as GPU to speed up. The trained model can be applied to various tasks, such as image recognition, speech recognition, natural language processing, etc.
Application reasoning, also known as reasoning or model reasoning, refers to the process of using trained models to predict or classify new input data. This process usually includes preparing input data, loading models, forward propagation and other steps. Among them, preparing the input data is to prepare the input data to be predicted and carry out necessary pre-processing, such as normalization and noise removal; Loading model is to load the trained model into memory for reasoning operation; Forward propagation is to input the prepared input data into the model for calculation and get the prediction results.
In practical application, The training and application reasoning of AI large model are interrelated. First, we need to get a good model through training; Then, the model can be applied to the actual scene, and new data can be inferred to achieve various intelligent applications. At the same time, with the continuous accumulation of data and the continuous optimization of the model, the model needs to be continuously trained and updated to improve its performance and accuracy.
