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Jun 21, 2023 · 1 min read · RNNs (Recurrent Neural Networks) have some problems that can affect their performance: Forgetting Distant Information: RNNs struggle to remember information from earlier steps in long sequences, making it difficult for them to capture long-term depe...
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Jun 21, 2023 · 1 min read · Backpropagation in an RNN (Recurrent Neural Network) works by propagating the error or loss information backward through time. It calculates the gradients of the model parameters with respect to the loss function, allowing the model to learn and adju...
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Jun 21, 2023 · 1 min read · RNNs can be categorized based on the relationship between the input and output sequences. Here are some commonly used types: One-to-One: The simplest type, where a single input is mapped to a single output, similar to a traditional feedforward neura...
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Jun 21, 2023 · 1 min read · RNNs (Recurrent Neural Networks) work by sequentially processing input data, one step at a time. At each step, the RNN takes an input and produces an output. It also maintains a hidden state, which acts as a memory of the previous steps. The hidden s...
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Jun 21, 2023 · 1 min read · RNNs (Recurrent Neural Networks) are needed because they can effectively process sequential data by capturing temporal dependencies. Unlike feedforward networks, RNNs have feedback connections that allow them to maintain and utilize information from ...
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