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  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: ...

    rawpixel.com  |  License details A Bidirectional LSTM (Long Short-Term Memory) is a type of Recurrent Neural Network (RNN) that processes input sequences in both forward and backward directions. This allows the model to capture both past and future contexts, improving performance on tasks like language modeling, sentiment analysis, and machine translation. Key aspects: Two LSTM layers: one processing the input sequence from start to end, and another from end to start Outputs from both layers are combined to form the final representation Transformers Transformers are a type of neural network architecture introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017. They're primarily designed for sequence-to-sequence tasks like machine translation, but have since been widely adopted for other NLP tasks. Key aspects: Self-Attention mechanism: allows the model to attend to all positions in the input sequence simultaneously Encoder-Decoder architect...