Back Propagation Back Propagation is the essence of neural net training. It is the practice of finetuning the weights of a neural net based on the error rate. obtained in the previous epoch. proper tuning of the weights ensures lower error rates, making the model model reliable by increasing its generalization. Advantages The computing time is minimized if the weights chosen are small at the beginning. The mathematical formula of back propagation can be applied to any network. Disadvantages It has more number of learning steps, and also the learning phase has intensive calculations. The training may sometimes cause temporal instability to  the system The network may get trapped in a local minima. Applications Data compression Image compression Face recognition Optical character recognition Control problems Non-linear simulation Fault detection problem Load forecasting problem https://rgpvnotesforcsestudents.blogspot.com/2021/03/neural-network-architecture.html https://rgpvnotesforcses