Gated Recurrent Unit (GRU)

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Получаемые навыки

Recurrent Neural Network, Artificial Neural Network, Deep Learning, Long Short-Term Memory (ISTM)

Рецензии

4.8 (оценок: 14,436)
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Great hands on instruction on how RNNs work and how they are used to solve real problems. It was particularly useful to use Conv1D, Bidirectional and Attention layers into RNNs and see how they work.

Hope can elaborate the backpropagation of RNN much more. BP through time is a bit tricky though we do not need to think about it during implementation using most of existing deep learning frameworks.

Из урока
Recurrent Neural Networks
Learn about recurrent neural networks. This type of model has been proven to perform extremely well on temporal data. It has several variants including LSTMs, GRUs and Bidirectional RNNs, which you are going to learn about in this section.

Преподаватели

  • Andrew Ng

    Andrew Ng

    CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain
  • Head Teaching Assistant - Kian Katanforoosh

    Head Teaching Assistant - Kian Katanforoosh

    Lecturer of Computer Science at Stanford University, deeplearning.ai, Ecole CentraleSupelec
  • Teaching Assistant - Younes Bensouda Mourri

    Teaching Assistant - Younes Bensouda Mourri

    Mathematical & Computational Sciences, Stanford University, deeplearning.ai

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