RNN Block
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A RNN Block is a neural network block that specifically utilizes RNN architecture to process sequences of data by maintaining a 'memory' of previous inputs using its internal state.
- Context:
- It can (typically) process Time Series data, Natural Language Text, and any other form of sequential data where the order of inputs is significant.
- It can (often) be used in tasks like Language Modeling, Speech Recognition, and Time Series Forecasting.
- It can range from basic RNN blocks with a single layer to complex architectures involving multiple layers and additional features like Dropout or Gating Mechanisms like in an LSTM or GRU.
- It can propagate errors back through time during training, a process known as Backpropagation Through Time (BPTT).
- It can suffer from issues like Vanishing Gradient Problem and Exploding Gradient Problem, which can be mitigated by using advanced RNN types like LSTMs or GRUs.
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- Example(s):
- an LRU Block.
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- Counter-Example(s):
- Feedforward Neural Networks, which do not have the capability to maintain internal state over time.
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- See: Recurrent Neural Network, LSTM, GRU, Neural Network Layer, Backpropagation.