how to do incremental deep learning on data stream that can adapt to constantly generated data points? I am currently trying to learn a deep learning model on a data stream, which constantly generate new data points over time.  The goal is to generate a real-time DL model that can well adapt to newly generated data while does not affect the prediction for old data too much.  I am wondering if anyone know what might be the best way to do this &quot;incremental deep learning on data stream&quot;?  Any information would be much appreciated, thank you!
What you are describing does not require a change in the deep learning models you would use but a change in how to estimate them: you need an online estimation procedure rather than an off-line one. You can have a look at the seminal work of Botou [1998], or Chapter 8 of the book by Goodfellow et al. (2016).