There are roads in the mountains of books, and you work hard to learn from the sea.
How is the AI model trained?
data preparation
Before training an AI model, a large amount of data sets need to be prepared. The quality and diversity of the dataset directly affect the effect of model training. The main tasks of data set preparation include:
Data cleaning : remove unnecessary information such as noise, errors, and repetitions in the data.
Data labeling : Label or classify the data accordingly. For example, for image data, the object or scene category in the image can be marked, and for text data, the meaning or type of sentences or words can be marked.
Data splitting : Divide the data set into training set, validation set and test set. The training set is used for model training, the validation set is used to verify the performance and parameter adjustment of the model, and the test set is used to evaluate the final performance of the model.
Model Selection
After the data is ready, it is necessary to select a suitable model for training. Typically, the process of selecting a model involves several factors:
Application scenarios : Different application scenarios require different models. For example, convolutional neural network models can be used for image classification problems, and recurrent neural network or transformer models can be used for natural language processing problems.
Model performance : It is necessary to select an appropriate model based on performance indicators such as model accuracy, generalization performance, and calculation speed.
Hardware resources : Some models require higher computing resources, such as GPU or TPU acceleration, and the limitation of hardware resources needs to be considered.
Model initialization
The initialization of the model refers to the random initialization of the parameters of the model before starting the training. In deep learning, the initialization of the model is very important, because the choice of initial values will affect the training speed and performance of the model. Usually, the parameter initialization of the model needs to follow certain rules and principles, such as avoiding the value of the parameter being too large or too small, avoiding the correlation between parameters, etc.
Selection of loss function
The loss function is an index used to measure the performance of the model on the training data. Usually, the corresponding loss function needs to be selected according to the characteristics of the problem. Commonly used loss functions include mean square error, cross entropy, KL divergence, etc.
Backpropagation Algorithm
After determining the loss function, it is necessary to use the backpropagation algorithm to calculate the gradient of the model parameters for the loss function. The backpropagation algorithm is an efficient method of calculating the gradient, which can use the chain rule to calculate the gradient of each parameter in the model for the loss function.
Parameter optimization
Based on the calculated gradients, an optimization algorithm needs to be used to update the parameters in the model. The goal of the optimization algorithm is to minimize the loss function so that the prediction results of the model are more accurate. Commonly used optimization algorithms include gradient descent, Adam, etc.
Model Evaluation
After model training is complete, the model needs to be evaluated to determine its performance and accuracy. Common ways to evaluate a model include calculating the loss function of the model, calculating the accuracy of the model, viewing the confusion matrix of the model, and so on.
Hyperparameter adjustment
Hyperparameters are parameters that need to be manually set before model training, such as learning rate, batch size, regularization coefficient, etc. The adjustment of hyperparameters can directly affect the training effect and performance of the model, so repeated trials and adjustments are required to obtain the optimal hyperparameter settings.
Model saving and deployment
After completing the training and evaluation of the model, the model can be saved and deployed. The method of model preservation includes serialization, compression, etc., and the deployment method includes integrating the model into the application program, providing model services in the form of API, etc.
The above are the detailed steps of AI model training. It should be noted that model training is a complex process that requires continuous adjustment and optimization in practice. At the same time, it is also necessary to pay attention to the quality and diversity of data to ensure that high-quality models are trained.
How to prevent programmers from deleting libraries and running away?
Authority control The authority that is as sensitive as data will be strictly controlled, such as the authority control and prevention of personnel: Operation and maintenance personnel need to perform two-factor authentication; firewalls or other security devices restrict the network, only allowing access to the core from the bastion host Assets; account passwords of system resources such as databases are hosted in the bastion host, and the bastion host automatically changes passwords on a regular basis, so that operation and maintenance personnel do not need to know the account passwords of system resources such as databases; the access rights of background resources are strictly controlled, and the principle of minimizing access rights is achieved. Assign minimum access rights to different operation and maintenance personnel; set the treasury mode for high-risk operation commands such as databases, and trigger blocking, secondary approval and other operations when executing high-risk commands; set higher-level security restrictions according to actual conditions, such as when logging in to the bastion host IP address, MAC address restrictions, time limit for logging in to the bastion host, etc.
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