• Understanding LSTM Networks

  • 2024/11/02
  • 再生時間： 8 分
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Understanding LSTM Networks

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  • In this episode we break down 'Understanding LSTM Networks', the blog post from "colah's blog" provides an accessible explanation of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network specifically designed to handle long-term dependencies in sequential data. The author starts by explaining the limitations of traditional neural networks in dealing with sequential information and introduces the concept of recurrent neural networks as a solution. They then introduce LSTMs as a special type of recurrent neural network that overcomes the issue of vanishing gradients, allowing them to learn long-term dependencies. The post includes a clear and detailed explanation of how LSTMs work, using diagrams to illustrate the flow of information through the network, and discusses variations on the basic LSTM architecture. Finally, the author highlights the success of LSTMs in various applications and explores future directions in recurrent neural network research.

    Audio : (Spotify) https://open.spotify.com/episode/6GWPmIgj3Z31sYrDsgFNcw?si=RCOKOYUEQXiG_dSRH7Kz-A

    Paper: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
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あらすじ・解説

In this episode we break down 'Understanding LSTM Networks', the blog post from "colah's blog" provides an accessible explanation of Long Short-Term Memory (LSTM) networks, a type of recurrent neural network specifically designed to handle long-term dependencies in sequential data. The author starts by explaining the limitations of traditional neural networks in dealing with sequential information and introduces the concept of recurrent neural networks as a solution. They then introduce LSTMs as a special type of recurrent neural network that overcomes the issue of vanishing gradients, allowing them to learn long-term dependencies. The post includes a clear and detailed explanation of how LSTMs work, using diagrams to illustrate the flow of information through the network, and discusses variations on the basic LSTM architecture. Finally, the author highlights the success of LSTMs in various applications and explores future directions in recurrent neural network research.

Audio : (Spotify) https://open.spotify.com/episode/6GWPmIgj3Z31sYrDsgFNcw?si=RCOKOYUEQXiG_dSRH7Kz-A

Paper: https://colah.github.io/posts/2015-08-Understanding-LSTMs/
続きを読む 一部表示

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