Network transformer is not a deep neural network. Network transformer is an electronic component, while deep neural network is a machine learning model.
Network transformer is an electronic component, mainly used in the field of network communication.
It can realize the connection between Ethernet and terminal interface. Its main functions include signal transmission, impedance matching, clutter suppression and high-voltage isolation. The purpose of this is to ensure the stability and security of data during transmission. Network transformer works on the principle of electromagnetic induction and receives signals through the primary coil. This means that a changing magnetic field is generated in the magnetic core, and then a voltage is induced in the secondary coil to complete the signal transmission.
Network transformer works on the principle of electromagnetic induction, using the signal received by the primary coil to generate a changing magnetic field in the magnetic core, and then a voltage is induced in the secondary coil to complete the signal transmission. This design can effectively suppress the interference of common-mode and differential-mode signals, enhance the transmission distance of the signal and the anti-interference ability of the system.

Deep neural network is a machine learning model composed of multiple levels.
Each level contains multiple neurons, which predict or classify by learning data features.
DNN has achieved remarkable results in the fields of image recognition, speech recognition, natural language processing, etc. due to its powerful feature extraction and abstraction capabilities.
In addition, DNN is also widely used in tasks such as speech recognition, machine translation, sentiment analysis, and text generation.
Therefore, the network transformer is not a deep neural network. The network transformer is an electronic component, while the deep neural network is a machine learning model.