1. 池化层: Extract important information, remove unimportant information, reduce parameters, reduce computational overhead, and prevent overfitting.
2. 全连接层（FC）: It acts as a "classifier" in the entire convolutional neural network.
3. 激活函数: Introduce nonlinear factors to neurons, thereby improving the expressive ability of the network.
4. backbone: 主干（骨干）网络, generally refers to the network that extracts features, and its function is to extract the information in the picture for use by the subsequent network.
5. 反向传播: In backpropagation, the movement of the network is backward, the error flows in from the outer layer with the gradient, passes through the hidden layer, and the weight is updated.
6. 超参数: It is a parameter whose value is set before starting the learning process, not the parameter data obtained through training.
7. normalization：Data standardization processing to solve the comparability between data indicators.
Batch NormOn the batch, normalize the input of each single channel;
Layer Normin the direction of the channel, normalize the input of each depth.
8. MLP: The Chinese name is 多层感知机, its essence is the neural network. It is proposed mainly to solve nonlinear problems that single-layer perceptrons cannot solve. Detailed blog introduction
transformer
