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This repository introduces Artificial Intelligence with a focus on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN). Initially intended as supplementary lecture material, it helps readers understand LSTM-RNN and its evolution since the 1990s. Modern research on LSTM-RNN uses updated notations and more concise derivations.

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Project of Numerical Analysis for Machine Learning

Project Overview

This repository includes a detailed report and a presentation based on the paper –Understanding LSTM – a tutorial into Long Short-Term Memory Recurrent Neural Networks. This project aims to provide readers with an understanding of Long Short-Term Memory (LSTM) networks and the reasons for their status as one of the most potent dynamic classifiers.

Contents

  • Report: A comprehensive document that explains the concepts, methodologies, and applications of LSTM networks.
  • Presentation: A summarized version of the report, designed for quick consumption and presentation purposes.

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This repository introduces Artificial Intelligence with a focus on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN). Initially intended as supplementary lecture material, it helps readers understand LSTM-RNN and its evolution since the 1990s. Modern research on LSTM-RNN uses updated notations and more concise derivations.

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artificial-intelligence lstm rnn feedforward-neural-network lstm-neural-networks ffnn lstm-rnn

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