Artificial intelligence model training system and method
Through systematic data processing and model training methods, the problems of low training efficiency and insufficient generalization capabilities of traditional models are solved, efficient and accurate artificial intelligence model training is achieved, and model performance and training speed in multiple fields are significantly improved.
Patent Information
- Application Number
- CN202510672425.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-23
- Publication Date
- 2025-09-02
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
When traditional model training methods face large-scale, high-dimensional and complex data, there are problems such as low training efficiency, insufficient generalization capabilities of the model, and easy to fall into local optimal solutions, which is difficult to meet the ever-improving model performance requirements.
Systematic methods of data acquisition and preprocessing, model construction, model training and model evaluation and adjustment are adopted, including a variety of model construction options, distributed training algorithms, adaptive learning rate adjustment strategies and multiple evaluation indicators, and combined with feature engineering, regularization technology and optimization algorithms, model parameters and architecture are dynamically adjusted.
The accuracy and training efficiency of the model have been significantly improved, the image recognition accuracy has been increased to 85%, the F1 value of natural language processing has been increased to 0.80, the recall of the recommendation system has been increased to 0.6, the sensitivity of medical diagnosis has reached 0.85, the specificity is 0.88, and the training time has been shortened by 30-25%, which has greatly improved the processing speed and accuracy.
Smart Images
Figure CN120580482A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of artificial intelligence technology, and in particular to an artificial intelligence model training system and method. Background Art
[0002] In the field of artificial intelligence, model training is a key step in enabling the model to learn data features and make accurate predictions or decisions.
[0003] Traditional model training methods often suffer from low training efficiency, insufficient model generalization, and susceptibility to local optimal solutions when faced with large-scale, high-dimensional, and complex data. With the exponential growth of data volumes and the increasing demands on model performance in various application scenarios, the development of an efficient, accurate, and powerfully generalized artificial intelligence model training system and method is of great practical significance. Accordingly, the present invention proposes an artificial intelligence model training system and method. Summary of the Invention
[0004] The purpose of the present invention is to solve the shortcomings of the existing technology and to propose an artificial intelligence model training system and method.
[0005] In order to achieve the above object, the present invention adopts the following technical solutions:
[0006] An artificial intelligence model training method comprises the following steps:
[0007] S1. Data Collection and Preprocessing: Collect data from multiple data sources, such as databases, sensors, and web crawlers. Apply appropriate data cleaning techniques to remove noise and missing values based on different data types. Use normalization methods to map data to a specific range. Leverage feature engineering techniques, such as principal component analysis, to extract, select, and transform features to improve data quality for model training.
[0008] S2. Model Building: Provides neural networks, including fully connected neural networks, convolutional neural networks, recurrent neural networks, and multiple model building options such as decision trees and support vector machines. Users can select the appropriate model architecture and initialize parameters based on the task type and data characteristics.
[0009] S3. Model training: Using a distributed training algorithm, the training tasks are distributed to multiple computing nodes for parallel execution. Adaptive learning rate adjustment strategies and regularization techniques are introduced. Outputs are calculated through forward propagation. Depending on the task type, the cross entropy loss function is used for classification tasks, or the mean squared error loss function is used for regression tasks. Weights and biases are updated using the backpropagation algorithm.
[0010] S4. Model evaluation and adjustment: During and after training, use multiple evaluation indicators such as accuracy, recall, F1 value, and mean square error to comprehensively measure model performance. Based on the evaluation results, automatically adjust model parameters or architecture, such as the number of neural network layers, number of nodes, and learning rate, to optimize model performance.
[0011] Preferably, the data cleaning is performed on structured data by using statistical analysis methods to detect and process outliers and missing values, and on image data by using algorithms such as median filtering to remove noise.
[0012] Preferably, for the data normalization, for numerical data, methods such as minimum to maximum normalization and Z-score normalization can be selected to normalize the data to a specific interval.
[0013] Preferably, in addition to principal component analysis, the feature engineering may also use feature selection algorithms, such as chi-square test, information gain, etc., to screen key features.
[0014] Preferably, when constructing the model, the parameter initialization methods include random initialization, initialization based on a specific distribution, etc., to ensure the stability and efficiency of model training.
[0015] Preferably, the adaptive learning rate adjustment strategy, in addition to the Adagrad algorithm, can also adopt algorithms such as Adadelta, RMSProp, and Adam to dynamically adjust the learning rate and improve the training effect.
[0016] Preferably, during model evaluation and adjustment, if the model is underfitting, it can be optimized by increasing training data, reducing regularization intensity, adjusting model complexity, etc.
[0017] An artificial intelligence model training system, comprising:
[0018] Data collection and preprocessing module: collects data from various data sources, cleans, normalizes and performs feature engineering on different types of data to improve data quality and make it suitable for model training;
[0019] Model building module: provides a variety of model building options. Users can select the appropriate model architecture and initialize parameters based on the task type and data characteristics.
[0020] Training module: uses a distributed training algorithm to distribute training tasks to multiple computing nodes for parallel execution, and introduces adaptive learning rate adjustment strategies and regularization techniques for model training;
[0021] Evaluation module: Uses multiple evaluation metrics to comprehensively measure model performance during and after training;
[0022] Model adjustment module: automatically adjusts the model parameters or architecture based on the feedback from the evaluation module to optimize model performance;
[0023] Storage module: used to store raw data, preprocessed data, model parameters, training logs, and evaluation results, etc., using distributed storage technology to ensure data security and scalability.
[0024] Preferably, the data acquisition and preprocessing module has corresponding data acquisition interfaces and preprocessing processes for different data sources to achieve efficient data acquisition and processing.
[0025] Preferably, the model building module provides a visual model building interface to facilitate users to select model architecture and set parameters.
[0026] The present invention has the following beneficial effects:
[0027] 1. In image recognition, median filtering is used to remove salt and pepper noise, combined with normalization and LBP texture feature extraction, greatly improving the quality and feature richness of image data. In the field of natural language processing, HTML tags, special characters, and stop words are removed. With the help of Word2Vec word embedding and TF-IDF feature extraction, text data is effectively purified and deeply mined. The recommendation system improves data reliability and availability by cleaning abnormal behavior data, mean normalizing rating data, and performing SVD dimensionality reduction. Medical diagnosis comprehensively cleans medical data, using Gaussian filtering for denoising, normalization, and wavelet transform to extract image features, providing a higher-quality and more accurate data base for subsequent model training.
[0028] 2. The accuracy of image recognition jumped from 70% of ordinary methods to 85%, the number of misclassifications was significantly reduced, the F1 value of text classification for natural language processing increased from 0.65 to 0.80, the perplexity of language generation tasks decreased, the recall rate of the recommendation system increased from 0.4 to 0.6, and the hit rate increased to 0.45. In the field of medical diagnosis, the sensitivity of image diagnosis reached 0.85, and the specificity was 0.88. The sensitivity of indicator diagnosis was 0.80, and the specificity was 0.82, significantly improving the accuracy and reliability of disease diagnosis.
[0029] 3. The image recognition training time has been shortened from 30 hours to 10 hours, the number of natural language processing training iterations has been reduced from 200 to 120, the response time for the recommendation system to generate recommendation results has been shortened by 30%, the average diagnosis time for imaging diagnosis in medical diagnosis has been shortened by 25%, and the indicator diagnosis time has been shortened by 20%. This has greatly improved the processing speed in practical applications and can provide users with results and services faster.
[0030] 4. ResNet with a deep residual structure is used, recurrent neural networks or long short-term memory networks are used for natural language processing, neural collaborative filtering models are used for recommendation systems, and imaging diagnosis and indicator diagnosis are selected for medical diagnosis based on different data types. At the same time, in terms of training optimization, innovative methods are used to abandon the simple initialization and fixed learning rate algorithms of ordinary methods, and targeted initialization methods such as Kaiming initialization and Xavier initialization are used. Combined with stochastic gradient descent and optimization algorithms, time backpropagation algorithms and optimization, Adadelta algorithms, etc., the learning rate and parameters are dynamically adjusted to make model training more efficient and stable, and to achieve better performance faster. BRIEF DESCRIPTION OF THE DRAWINGS
[0031] Figure 1 This is a diagram showing some codes in the field of image recognition in an artificial intelligence model training method proposed in the present invention;
[0032] Figure 2 This is a diagram showing some codes in the field of natural language processing in an artificial intelligence model training method proposed in the present invention;
[0033] Figure 3 This is a diagram showing some codes in the recommendation system field of an artificial intelligence model training method proposed in the present invention;
[0034] Figure 4 This is a partial code display diagram for the medical diagnosis field in an artificial intelligence model training method proposed in this invention. DETAILED DESCRIPTION
[0035] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.
[0036] Example 1: Image Recognition Field
[0037] Step 1: Data collection and preprocessing;
[0038] Image data is collected from the image database and camera. For image data, in the data cleaning process, the median filter algorithm is used to remove salt and pepper noise. For an m×n image window, the median filter outputs the pixel value y ij The median value of all pixel values in the window after sorting.
[0039] Image normalization is done by mapping pixel values from [0, 255] to [0, 1] using the formula Where x is the original pixel value, for the central pixel p0, its neighboring pixel p i (i=1,…,8), the LBP value calculation formula is in
[0040] Step 2: Model construction;
[0041] Select a convolutional neural network (CNN) architecture, such as ResNet. Taking ResNet-18 as an example, it contains multiple convolutional layers, pooling layers, and fully connected layers. During initialization, the convolution kernel weights use the Kaiming initialization method, and the formula is where n in is the number of input neurons.
[0042] Step 3: Model training;
[0043] Propagation: In the convolution layer, assuming the input feature map is X, the convolution kernel is K, and the bias is b, the calculation formula for the output feature map Y is Where M, N are the convolution kernel sizes, after the activation function ReLU,
[0044] Calculate loss: Use the cross entropy loss function, as before. For image classification tasks, calculate the loss between the predicted category and the true category, backpropagate, calculate the gradient according to the loss function, update the convolution kernel weights and bias, and use the stochastic gradient descent (SGD) algorithm. The weight update formula is Where α is the learning rate.
[0045] Step 4: Model evaluation and adjustment: Calculate indicators such as accuracy and recall on the image validation set. If the model has low recognition accuracy for certain categories, adjust the convolution kernel size, increase the network depth, or change the data enhancement method, such as adding rotation, scaling, and other operations.
[0046] Example 2: Natural Language Processing
[0047] Step 1: Data collection and preprocessing;
[0048] Data is collected from text data sources such as news articles, social media, and books. When cleaning the data, HTML tags and special characters are removed. Stop words are removed using the stop word list in the NLTK library. Data normalization is achieved through word embedding, such as using the Word2Vec algorithm to map each word to a low-dimensional vector space. Assuming that the context window of word w is C, the objective function is maximized. To learn word vectors, where V is the vocabulary and p(c|w) is calculated by the softmax function.
[0049] Feature engineering can use the TF-IDF (term frequency - inverse document frequency) algorithm. The TF-IDF value of word w in document d is TF-IDF(w,d)=TF(w,d)×TDF(w), where TF(w,d) is the term frequency of word w in document d. N is the total number of documents, n w is the number of documents containing word w.
[0050] Step 2: Model construction;
[0051] Select a recurrent neural network (RNN) or its variant, long short-term memory network (LSTM). Take LSTM as an example, which includes an input gate i t 、Forget Gate t , output gate o t and memory unit C t ,During initialization, the weight matrix and bias vector are randomly initialized.
[0052] Step 3: Model training;
[0053] Forward propagation: The calculation formula of LSTM unit is as follows:
[0054] i t =σ(W ii x t +W hi h t-1 +b i )
[0055] f t =σ(W if x t +W hf h t-1 +b f )
[0056] C t =f t ⊙C t-1 +i t ⊙tanh(W ic x t +W hc h t-1 +b c )
[0057] o t =σ(W io x t +W ho h t-1 +b o )
[0058] h t =o t ⊙tanh(C t )
[0059] Where W is the weight matrix, b is the bias vector, σ is the sigmoid function, and ⊙ is the element-by-element multiplication.
[0060] Calculate the loss: For text classification tasks, the cross entropy loss function is used, and for language generation tasks, the negative log-likelihood loss function is used.
[0061] Backpropagation: Calculate gradients and update weights using the Backpropagation Through Time (BPTT) algorithm.
[0062] Step 4: Model evaluation and adjustment: Calculate indicators such as BLEU (for machine translation evaluation) and F1 value (for classification tasks) on the text verification set. If the model has repeated sentences when generating text, adjust the hidden layer size of the LSTM, increase the regularization strength, or use the beam search algorithm to improve the generation strategy.
[0063] Example 3: Recommendation System Field
[0064] Step 1: Data collection and preprocessing;
[0065] Data is collected from user behavior data (such as clicks, purchases, and ratings) and product information data. Data cleaning removes abnormal behavior data, such as a large number of unreasonable clicks in a short period of time. For data normalization, mean normalization is used for rating data. The formula is: Where r is the original score, is the average rating, σ r is the standard deviation of the score.
[0066] Feature engineering uses collaborative filtering features to construct a user-item rating matrix, and performs dimensionality reduction through singular value decomposition (SVD). Let the rating matrix be R, and after SVD decomposition, we get R=U∑V T , select the matrix U corresponding to the first k largest singular values k ,∑ k ,V k , reconstruct the rating matrix
[0067] Step 2: Model construction;
[0068] Use a matrix decomposition model or a deep learning-based recommendation model, such as Neural Collaborative Filtering (NCF). The NCF model consists of an embedding layer, a multi-layer perceptron (MLP) layer, and an output layer. During initialization, the embedding layer weights are randomly initialized.
[0069] Step 3: Model training;
[0070] Forward propagation: In the NCF model, the user and item embedding vectors are transformed nonlinearly through the MLP layer. Assume that the input of the MLP layer is z0, and after l layers of transformation, the output is z l , the formula is z l =σ(W l z l-1 +b l), and finally calculate the predicted score through the output layer
[0071] Calculate loss: use mean square error loss function Among them, D is the set of household-item pairs, r ui Rating for authenticity.
[0072] Back propagation: Calculate the gradient to update the weight using the Adadelta algorithm. The weight update formula is: Where E[ΔW 2 ] t-1 is the mean of the square of historical gradients, g t is the current gradient, and ∈ is a small constant to prevent division by zero.
[0073] Step 4: Model evaluation and adjustment;
[0074] Calculate indicators such as recall rate and hit rate on the recommendation system validation set. If the diversity of the recommendation results is insufficient, adjust the structure of the NCF model, such as increasing the width of the MLP layer or changing the dimension of the embedding layer.
[0075] Example 4: Medical diagnosis field
[0076] Step 1: Data collection and preprocessing;
[0077] Medical data is collected from the hospital's electronic medical record system and medical imaging equipment. Data cleaning removes erroneous records and incomplete data. For medical imaging data, Gaussian filtering is used to remove noise. The Gaussian filtering formula is: where σ is the standard deviation of the Gaussian kernel.
[0078] Data normalization For numerical medical indicators, such as blood pressure and heart rate, minimum-maximum normalization is used. For medical images, normalization to the [-1,1] interval is used, and the formula is
[0079] For medical images, feature engineering uses wavelet transform to extract features. For signal f(x), its wavelet transform Where a is the scale parameter, b is the translation parameter, and ψ is the wavelet basis function.
[0080] Step 2: Model construction;
[0081] Select convolutional neural network (CNN) for medical image diagnosis, or logistic regression model for disease prediction based on medical indicators. Taking CNN as an example, build a network structure suitable for the characteristics of medical images, such as the convolution layer design that increases the receptive field. When initializing, the weights use the Xavier initialization method, and the formula is where n in ,n outare the number of input and output neurons, respectively.
[0082] Step 3: Model training;
[0083] Forward propagation: Similar to CNN in image recognition, it performs convolution, activation and other operations in medical image processing. For a logistic regression model based on medical indicators, assuming that the input feature vector is x, the weight vector is w, and the bias is b, the predicted probability
[0084] Calculate loss: For medical image classification, the cross entropy loss function is used, and for the logistic regression model of disease prediction, the logarithmic loss function is used where y i is the true label, p i is the predicted probability.
[0085] Back propagation: CNN updates weights through the back propagation algorithm, and logistic regression updates weights through the gradient descent algorithm. The weight update formula is
[0086] Step 4: Model evaluation and adjustment;
[0087] Calculate indicators such as accuracy, sensitivity, and specificity on the medical data validation set. If the model has low diagnostic accuracy for rare diseases, oversampling technology can be used to increase the number of rare disease samples, or the model structure can be adjusted to enhance the learning ability of small samples.
[0088] Based on the above four embodiments, it can be seen that the innovative artificial intelligence model training method has shown significant beneficial effects in many aspects and has obvious advantages over ordinary methods. In the data processing link, the innovative method shows higher precision and depth. In image recognition, median filtering is used to remove salt and pepper noise, and normalization and LBP texture feature extraction are used to greatly improve the quality and feature richness of image data. In the field of natural language processing, HTML tags, special characters and stop words are removed, and with the help of Word2Vec word embedding and TF-IDF feature extraction, text data is effectively purified and deeply mined. The recommendation system improves the reliability and availability of data by cleaning abnormal behavior data, mean normalizing the scoring data, and SVD dimensionality reduction. Medical diagnosis comprehensively cleans medical data, uses Gaussian filtering for denoising, normalization and wavelet transform to extract image features, providing a higher quality and more accurate data foundation for subsequent model training.
[0089] Furthermore, innovative approaches have led to comprehensive improvements in model performance. Image recognition accuracy has jumped from 70% for conventional methods to 85%, with a significant reduction in misclassifications. The F1 score for text classification in natural language processing has increased from 0.65 to 0.80, while the perplexity of language generation tasks has been reduced. The recall rate of the recommendation system has increased from 0.4 to 0.6, and the hit rate has increased to 0.45. In the medical diagnosis field, imaging diagnostic sensitivity has reached 0.85, with a specificity of 0.88. Indicator diagnostic sensitivity has reached 0.80, with a specificity of 0.82, significantly improving the accuracy and reliability of disease diagnoses.
[0090] In addition, in terms of training efficiency, the image recognition training time was shortened from 30 hours to 10 hours, the number of natural language processing training iterations was reduced from 200 to 120, the response time for the recommendation system to generate recommendation results was shortened by 30%, the average diagnosis time for imaging diagnosis in medical diagnosis was shortened by 25%, and the indicator diagnosis time was shortened by 20%, which greatly improved the processing speed in practical applications and can provide users with results and services faster.
[0091] In addition, in terms of model architecture and training optimization, the innovative method adopts a more complex and task-adaptive model architecture. Image recognition uses ResNet with a deep residual structure, natural language processing uses recurrent neural networks (RNN) or long short-term memory networks (LSTM), the recommendation system uses a neural collaborative filtering (NCF) model, and medical diagnosis uses CNN (image diagnosis) and logistic regression (indicator diagnosis) according to different data types. At the same time, in training optimization, the innovative method abandons the simple initialization and fixed learning rate algorithm of ordinary methods, and adopts targeted initialization methods such as Kaiming initialization and Xavier initialization, combined with stochastic gradient descent and optimization algorithms, time backpropagation (BPTT) algorithm and optimization, Adadelta algorithm, etc., to dynamically adjust the learning rate and parameters, making model training more efficient and stable, and able to achieve better performance faster.
[0092] Table 1: Comparison of experimental data between Example 1 and Comparative Example
[0093] Comparison items Common methods Image Recognition Implementation Methods Accuracy 70％ 85％ Training time 30 hours 10 hours Number of misclassifications 3000 1500
[0094] Table 2: Comparison of experimental data between Example 2 and Comparative Example
[0095]
[0096] Table 3: Comparison of experimental data between Example 3 and Comparative Example
[0097]
[0098]
[0099] Table 4: Comparison of experimental data between Example 4 and Comparative Example
[0100] Comparison items Common methods Medical diagnosis implementation Sensitivity (diagnostic imaging) 0.7 0.85 Specificity (diagnostic imaging) 0.75 0.88 Sensitivity (indicator diagnosis) 0.65 0.80 Specificity (indicator diagnosis) 0.7 0.82
[0101] From the comparison in the above table, it can be clearly seen that in different application scenarios, the image recognition field has improved recognition accuracy and efficiency through targeted image preprocessing and optimized CNN training. The natural language processing field has enhanced semantic understanding and generation capabilities with the help of specific text processing and LSTM optimization. The recommendation system field uses unique data processing and NCF models to improve recommendation accuracy and diversity. The medical diagnosis field relies on professional medical data processing and model training to improve diagnostic reliability. Compared with ordinary methods, they have significant advantages in data processing, model selection and training optimization.
[0102] Specifically, in each embodiment, key code examples and corresponding analysis are written in Python.
[0103] Furthermore, if Figure 1 As shown in the figure, in the field of image recognition, median filtering is used to remove image noise, image normalization is performed to unify the data scale, and the LBP algorithm is used to extract texture features, providing more representative data for model training. The constructed CNN model consists of multiple convolutional layers, pooling layers, and fully connected layers. This structure can effectively extract the spatial features of the image. The SGD optimizer and the categorical cross-entropy loss function are used for training. During model evaluation, the loss value and accuracy are returned to measure model performance.
[0104] Furthermore, if Figure 2 As shown in the figure, in the field of natural language processing, text is preprocessed to remove stop words and convert words to lowercase. Word embeddings are obtained by training the Word2Vec model, and text features are calculated using TF-IDF. The constructed LSTM model is suitable for processing sequential data and can capture long-range dependencies in text. The model is trained using the Adam optimizer and the categorical cross-entropy loss function, and the evaluation metrics are also loss and accuracy.
[0105] Furthermore, Figure 3 As shown in the figure, in the field of recommendation systems, the rating data is normalized by mean and standard deviation, the rating matrix is reduced in dimensionality through singular value decomposition, and potential features are mined. The constructed neural collaborative filtering (NCF) model combines the embedding vectors of users and items, and predicts the user's rating of the item through dot product operations. It is trained using the Adadelta optimizer and the mean square error loss function, and the loss value is returned during evaluation to evaluate the accuracy of the model's predicted ratings.
[0106] Furthermore, if Figure 4As shown in the figure, in the field of medical diagnosis, Gaussian filtering is used to denoise medical images, images are normalized to the range [-1, 1], and features are extracted through wavelet transform. A CNN model is constructed for image diagnosis, trained using the Adam optimizer and the categorical cross-entropy loss function. A logistic regression model is used for training medical indicator data. Model evaluation involves calculating the accuracy, recall, and specificity of both the CNN and logistic regression models to comprehensively assess their performance in medical diagnosis tasks.
[0107] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.
Claims
1. An artificial intelligence model training method, characterized in that: The following steps are involved: S1. Data Collection and Preprocessing: Collect data from multiple data sources, such as databases, sensors, and web crawlers. Apply appropriate data cleaning techniques to remove noise and missing values based on different data types. Use normalization methods to map data to a specific range. Leverage feature engineering techniques, such as principal component analysis, to extract, select, and transform features to improve data quality for model training. S2. Model Building: Provides neural networks, including fully connected neural networks, convolutional neural networks, recurrent neural networks, and multiple model building options such as decision trees and support vector machines. Users can select the appropriate model architecture and initialize parameters based on the task type and data characteristics. S3. Model training: Using a distributed training algorithm, the training tasks are distributed to multiple computing nodes for parallel execution. Adaptive learning rate adjustment strategies and regularization techniques are introduced. Outputs are calculated through forward propagation. Depending on the task type, the cross entropy loss function is used for classification tasks, or the mean squared error loss function is used for regression tasks. Weights and biases are updated using the backpropagation algorithm. S4. Model evaluation and adjustment: During and after training, use multiple evaluation indicators such as accuracy, recall, F1 value, and mean square error to comprehensively measure model performance. Based on the evaluation results, automatically adjust model parameters or architecture, such as the number of neural network layers, number of nodes, and learning rate, to optimize model performance.
2. The artificial intelligence model training method according to claim 1, characterized in that: The data cleaning is aimed at structured data, using statistical analysis methods to detect and process outliers and missing values, and for image data, using algorithms such as median filtering to remove noise.
3. The artificial intelligence model training method according to claim 1, characterized in that: For numerical data, the data normalization can be performed using methods such as minimum to maximum normalization and Z-score normalization to normalize the data to a specific range.
4. The artificial intelligence model training method according to claim 1, characterized in that: In addition to principal component analysis, the feature engineering may also use feature selection algorithms, such as chi-square test, information gain, etc., to screen key features.
5. The artificial intelligence model training method according to claim 1, characterized in that: When building the model, the methods of initializing parameters include random initialization, initialization based on specific distribution, etc., to ensure the stability and efficiency of model training.
6. The artificial intelligence model training method according to claim 1, characterized in that: In addition to the Adagrad algorithm, the adaptive learning rate adjustment strategy can also use algorithms such as Adadelta, RMSProp, and Adam to dynamically adjust the learning rate and improve the training effect.
7. The artificial intelligence model training method according to claim 1, characterized in that: During model evaluation and adjustment, if the model is underfitting, it can be optimized by increasing training data, reducing regularization intensity, adjusting model complexity, etc.
8. An artificial intelligence model training system, characterized in that: include: Data collection and preprocessing module: collects data from various data sources, cleans, normalizes and performs feature engineering on different types of data to improve data quality and make it suitable for model training; Model building module: provides a variety of model building options. Users can select the appropriate model architecture and initialize parameters based on the task type and data characteristics. Training module: uses a distributed training algorithm to distribute training tasks to multiple computing nodes for parallel execution, and introduces adaptive learning rate adjustment strategies and regularization techniques for model training; Evaluation module: Uses multiple evaluation metrics to comprehensively measure model performance during and after training; Model adjustment module: automatically adjusts the model parameters or architecture based on the feedback from the evaluation module to optimize model performance; Storage module: used to store raw data, preprocessed data, model parameters, training logs, and evaluation results, etc., using distributed storage technology to ensure data security and scalability.
9. An artificial intelligence model training system according to claim 8, characterized in that: The data acquisition and preprocessing module has corresponding data acquisition interfaces and preprocessing processes for different data sources, thereby achieving efficient data acquisition and processing.
10. An artificial intelligence model training system according to claim 8, characterized in that: The model building module provides a visual model building interface to facilitate users to select model architecture and set parameters.