Online Deep Learning

Online deep learning focuses on training deep neural networks using continuously arriving data streams, aiming to achieve both fast learning speeds and high model accuracy. Current research explores novel architectures like multi-learner systems and auto-encoders, along with algorithmic improvements to address challenges such as concept drift and evolving feature spaces, often employing techniques like just-in-time compilation for efficient inference. This field is crucial for real-time applications like intrusion detection and in-browser AI, offering significant advantages in adaptability and reduced reliance on large, pre-collected datasets.