Data collection: Collect large amounts of data related to the task, which can be images, text, audio, video, etc.

Data cleaning: remove irrelevant data and deal with missing values ​​and outliers.

Data standardization: Convert data to a unified scale.

Data segmentation: Divide the data set into training set, validation set and test set to facilitate model training and evaluation.

Network structure design: Determine the number of layers of the network, the number of neurons in each layer, and the connection method (fully connected, convolution, loop, etc.).

Activation function selection: Choose an appropriate activation function for each layer, such as ReLU, Sigmoid, Tanh, etc.

Loss function definition: Select the loss function according to the task type, such as cross-entropy loss for classification tasks and mean square error for regression tasks.

Optimizer selection: Select the optimization algorithm for weight update, such as SGD, Adam, RMSprop, etc.

Input data: Input the normalized data into the first layer of the network.

Calculate activation: The neurons in each layer calculate activation values ​​based on the weights and the output of the previous layer, and apply the activation function.

Output result: After multi-layer calculations, the network outputs the final result, which may be a probability distribution for a classification task or a continuous value for a regression task.

Calculate the loss: Use the loss function to compare the network output and the real label to get the loss value.

Backpropagation: Starting from the output layer, gradients are calculated layer by layer, and weights and biases are updated.

Gradient Descent: Update network parameters based on gradient and learning rate.

Iterative process: Repeat the process of forward propagation, loss calculation, back propagation and weight update until a predetermined stopping condition is reached.

Regularization: Use regularization techniques (such as weight decay, dropout) to prevent overfitting.

Hyperparameter adjustment: Adjust hyperparameters such as learning rate, batch size, and network structure to optimize model performance.

Model evaluation: Evaluate model performance on the validation set and adjust hyperparameters.

Generalization ability test: evaluate the generalization ability of the model on the test set.

Model deployment: Deploy the trained model to the server or device for practical applications.

Model monitoring: Monitor the performance of your model in production and make adjustments as needed.

New data collection: Continuously collect new data to reflect changes in the environment.

Model updates: Models are trained and updated regularly with new data to keep the model accurate and relevant.