Chapter 6: Recurrent Neural Networks (RNNs) and LSTMs
Traditional neural networks face significant challenges when processing sequential data due to their inherent design, which treats each input as an isolated entity without considering the context provided by previous inputs. This limitation is particularly problematic for tasks that require understanding temporal relationships or patterns that unfold over time. To address this shortcoming, researchers developed Recurrent Neural Networks (RNNs), a specialized class of neural networks specifically engineered to handle sequential information.
The key innovation of RNNs lies in their ability to maintain an internal hidden state, which acts as a form of memory, carrying relevant information from ...