Tag: RNN
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Long Short-Term Memory (LSTM): Overcoming the Challenges of Sequential Data
Long Short-Term Memory (LSTM) networks, introduced in 1997 by Hochreiter and Schmidhuber, are a type of RNN designed to overcome vanishing gradient issues. With a gated architecture, LSTMs excel in retaining long-term dependencies, making them ideal for tasks like NLP, speech recognition, and time series forecasting. Though transformers now dominate, LSTMs remain vital for real-time…
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Recurrent Neural Networks (RNNs): Unlocking Sequence Data in AI
Recurrent Neural Networks (RNNs) are specialized neural networks designed for sequential data, such as text, audio, and time series. They incorporate memory through loops, enabling context-aware processing. Despite challenges like vanishing gradients, RNN variants like LSTMs and GRUs improved long-term memory capabilities. While largely replaced by transformers, RNNs remain essential for real-time and compact sequence…