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About RNN Notes
This document provides an overview of implementing a basic recurrent neural network (RNN) in NumPy. It discusses the forward propagation process, including defining the input, hidden state, and prediction dimensions. It also outlines the key steps of implementing an RNN, which are to first code the calculations for a single time step, and then loop over the time steps to process the full input sequence. Finally, it introduces the concept of an RNN cell as performing the operations for a single time step, and provides a figure illustrating this.
Detailed Information
| Author: | ['Jatin Dhingra'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 36 |
| Language: | English |
| Format: | |
| Price: | FREE |
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