Think Different

  Long Short-Term Memory (LSTM) Networks LSTMs are a type of Recurrent Neural Network (RNN) designed to handle sequential data with long-term dependencies. Key Features: Cell State: Preserves information over long periods. Gates: Control information flow (input, output, and forget gates). Hidden State: Temporary memory for short-term information. Related Technologies: Recurrent Neural Networks (RNNs): Basic architecture for sequential data. Gated Recurrent Units (GRUs): Simplified version of LSTMs. Bidirectional RNNs/LSTMs: Process input sequences in both directions. Encoder-Decoder Architecture: Used for sequence-to-sequence tasks. Real-World Applications: Language Translation Speech Recognition Text Generation Time Series Forecasting GRUs are an alternative to LSTMs, designed to be faster and more efficient while still capturing long-term dependencies. Key Differences from LSTMs: Simplified Architecture: Fewer gates (update and reset) and fewer state vectors. Faster Computation: ...