Dear Doctor

"The Fourier Transform can help extract relevant features from time series data by analyzing its frequency components. This is crucial for tasks like anomaly detection, trend analysis, and forecasting. Natural Language Processing: Text data, when represented as a sequence of words, can be treated as a discrete signal."

In image processing and computer vision tasks, the Fourier Transform can be used to enlarge the field of view of convolutional (or other) neural networks. It can therefore increase the speed ot training and inference of such networks. An example of the use of FT in neural nets is the paper "FNet: Mixing Tokens with Fourier Transforms". It is also used for inpainting in "LaMa: Resolution-robust Large Mask Inpainting with Fourier Convolutions".

Using the Fourier transform we can reduce the complexity of such calculation and can make the model work faster.