Recurrent neural networks simply explained. #rnn

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Recurrent Neural Networks (RNNs) are a foundational concept in deep learning, especially for tasks involving sequential data like text, speech, or time series. Unlike traditional feedforward neural networks, RNNs use hidden states, which acts as a sort of "memory", allowing them to retain information from previous inputs. The hidden state gets updated at each time step, enabling the network to consider past context when making predictions. For example, in natural language processing (NLP), this capability helps the model understand the structure and meaning of a sentence by remembering earlier words as it processes later ones. RNNs are widely used in applications such as machine translation and speech recognition. RNNs do have limitations though, and one of them is struggling with long-term dependencies and sequences. Its "memory" degrades over time, so it doesn't quite remember that much from for example, 100 timesteps ago. To address this, more advanced variants like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks have been developed, offering better performance on longer sequences. Join our Al community for more posts like this @Giffah_Alexander #machinelearning #statistics #mathematics #rnn #computerscience #coding #science #education #datascience #deeplearning