The backpropagation algorithm is currently the most commonly used and effective method for training neural networks.
The main principles of backpropagation are:
1. **Forward Propagation**: Data flows from the input layer through the hidden layers to the output layer.
2. **Error Calculation and Propagation**: The error between the model's output and the actual result is calculated and then propagated backward. This means the error is transmitted from the output layer back through the hidden layers to the input layer.
3. **Iteration**: During backpropagation, the model's parameters are continuously adjusted based on the error. This process iterates through the first two steps until the model meets the training termination criteria.
The two most critical steps are: (1) propagating the error backward, and (2) continuously adjusting the model's parameters based on the error.
These steps are collectively known as optimization methods, with gradient descent being commonly used.
The backpropagation algorithm is currently the most commonly used and effective method for training neural networks.
The main principles of backpropagation are:
1. **Forward Propagation**: Data flows from the input layer through the hidden layers to the output layer.
2. **Error Calculation and Propagation**: The error between the model's output and the actual result is calculated and then propagated backward. This means the error is transmitted from the output layer back through the hidden layers to the input layer.
3. **Iteration**: During backpropagation, the model's parameters are continuously adjusted based on the error. This process iterates through the first two steps until the model meets the training termination criteria.
The two most critical steps are: (1) propagating the error backward, and (2) continuously adjusting the model's parameters based on the error.
These steps are collectively known as optimization methods, with gradient descent being commonly used.