Recurrent Neural Networks (RNNs) are a class of neural networks designed to handle sequential data by retaining information from previous steps through internal memory. Unlike traditional feedforward networks, RNNs have loops that allow them to process sequences of inputs, making them suitable for tasks like time series prediction, natural language processing, and speech recognition. However, RNNs can struggle with learning long-term dependencies due to vanishing or exploding gradient problems, which led to the developing of more advanced architectures like Long Short-Term Memory (LSTM). Here are some resources to get started with RNNs:
Long Short-Term Memory (LSTM) networks are a type of specialized Recurrent Neural Network (RNN) designed to capture long-term dependencies in sequential data. Unlike traditional RNNs, LSTMs use a memory cell and gating mechanisms—input, forget, and output gates—to control the flow of information, allowing them to retain important information over extended time periods and mitigate issues like vanishing gradients. This makes LSTMs particularly effective in tasks such as language modeling, time series forecasting, and speech recognition, where understanding context over long sequences is crucial. Here are some resources to explore LSTMs: