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Variants Of Recurrent Neural Networks Architectures Training Ppt

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Presenting Variants of Recurrent Neural Networks Architectures. These slides are 100 percent made in PowerPoint and are compatible with all screen types and monitors. They also support Google Slides. Premium Customer Support available. Suitable for use by managers, employees, and organizations. These slides are easily customizable. You can edit the color, text, icon, and font size to suit your requirements.

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Slide 1

This slide depicts three variants of Recurrent Neural Networks architectures. These include bidirectional recurrent neural networks, long short-term memory, and gated recurrent units.

Slide 2

This slide talks about Bidirectional Recurrent Neural Network as an architecture. Bidirectional RNNs pull in future data to increase accuracy, whereas unidirectional RNNs can only draw on previous inputs to create predictions about the current state.

Slide 3

This slide gives information about Long Short Term Memory as an architecture. LSTM is a well-known RNN design developed by Sepp Hochreiter and Juergen Schmidhuber to address the vanishing gradient problem.

Instructor’s Notes: In the deep layers of the neural network, LSTMs have "cells" that have three gates: input gate, output gate, and forget gate. These gates regulate the flow of data required to forecast the network's output.

Slide 4

This slide talks about Gated Recurrent Units as an architecture. This RNN version is similar to LSTMs because it also works to solve RNN models' short-term memory problem. It employs hidden states instead of "cell states" to govern information, and instead of three gates, it only has two—a reset gate and an update gate.

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