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• 1. 手把手搭建循环神经网络(RNN)_Jack_0601的博客-CSDN博客_rnn …

  csdn.net

  Link: https://blog.csdn.net/weixin_43509263/article/details/89190120

  Description: Apr 11, 2019 · 这是官方教程中同一个系列的文章，总共有3篇： 第一篇，教你搭建一个字母级别（character-level）的RNN，对名字进行分类，是一个分类的任务。第二篇，教你搭建一个字母级别（character-level）的RNN，生成名字，是一个自然语言生成的任务。第三篇，教你搭建一个Seq2Seq的RNN，进行机器翻译，也是一个 ...

• 2. Recurrent neural network - Wikipedia

  wikipedia.org

  Link: https://en.wikipedia.org/wiki/Recurrent_neural_network

  Description: A recurrent neural network (RNN) is a class of artificial neural networks where connections between nodes form a directed or undirected graph along a temporal sequence. This allows it to exhibit temporal dynamic behavior. Derived from feedforward neural networks, RNNs can use their internal state (memory) to process variable length sequences of inputs. ...

• 3. Time Series Classification Tutorial with LSTM Recurrent Neural

  omdena.com

  Link: https://omdena.com/blog/time-series-classification-model-tutorial/

  Description: Jun 12, 2022 · Our Model: The Recurrent Neural Network + Single Layer Perceptron. We need a deep learning model capable of learning from time-series features and static features for this problem. Hence we construct a single layer perceptron (SLP) and a bi-directional LSTM using Keras and TensorFlow.. The intuition behind the approach is that the bi-directional RNN will …

• 4. Vision Transformer 超详细解读 (原理分析+代码解读) (一) - 知乎

  zhihu.com

  Link: https://zhuanlan.zhihu.com/p/340149804

  Description: 如果假设是一个single directional的RNN，那当输出 b_4 时，默认 a_1,a_2,a_3,a_4 都已经看过了。如果假设是一个bi-directional的RNN，那当输出 b_{任意} 时，默认 a_1,a_2,a_3,a_4 都已经看过了。RNN非常擅长于处理input是一个sequence的状况。 那RNN有什么样的问题呢？

• 5. Transformer Neural Network In Deep Learning – Overview

  geeksforgeeks.org

  Link: https://www.geeksforgeeks.org/transformer-neural-network-in-deep-learning-overview/

  Description: Apr 11, 2022 · This standard feedforward neural network at LSTM has a feedback connection. It can not only process single data point, but also the entire sequence of data. With LSTM or long short term memory, it has something like, you know, we can feed a longer sequence compared to what it was with bi-directional RNN or RNNs. So why is LSTM better than RNN?

• 6. GitHub - rasbt/deeplearning-models: A collection of various …

  github.com

  Link: https://github.com/rasbt/deeplearning-models

  Description: A simple single-layer RNN with packed sequences to ignore padding characters (IMDB) TBD: TBD: ... RNN with GRU cells (IMDB) TBD: TBD: Multilayer bi-directional RNN (IMDB) TBD: TBD: Bidirectional Multi-layer RNN with LSTM with Own Dataset in CSV Format (AG News) TBD: TBD: Many-to-Many / Sequence-to-Sequence. Title Dataset Description

• 7. Write your own custom Attention layer: Easy, intuitive guide

  towardsdatascience.com

  Link: https://towardsdatascience.com/create-your-own-custom-attention-layer-understand-all-flavours-2201b5e8be9e

  Description: Dec 03, 2020 · Inputs to this ‘Dense single MLP — single unit layer’ will be all the hidden states of the LSTM sequence from the previous step. So we have 19 inputs for every tweet to this layer. Each input continues to have 256 dimensions. ... They simply take the bi-directional RNN output from both encoder and decoder. They also suggest using only the ...

• 8. GitHub - brightmart/text_classification: all kinds of text

  github.com

  Link: https://github.com/brightmart/text_classification

  Description: In NLP, text classification can be done for single sentence, but it can also be used for multiple sentences. we may call it document classification. Words are form to sentence. And sentence are form to document. ... by using bi-directional rnn to encode story and query, performance boost from 0.392 to 0.398, increase 1.5%.

• 9. 【边缘检测】BDCN：Bi-Directional Cascade Network for Perceptual …

  csdn.net

  Link: https://blog.csdn.net/jiaoyangwm/article/details/115941412

  Description: Apr 21, 2021 · 目录 摘要 1.介绍 2.相关工作 3.方法 3.1公式 3.2bdcn的架构 3.3网络训练 4.实验 4.1数据集 摘要 利用多尺度表示对于提高不同尺度对象的边缘检测至关重要。为了利用多尺度提取边缘，我们提出了一种全向级联网络（bdcn）结构，在该结构中，单个层由具有特定比例的标记边缘进行监督，而不是直接对所有 ...

• 10. Long short-term memory (LSTM) with Python - Alpha Quantum

  alpha-quantum.com

  Link: https://www.alpha-quantum.com/blog/long-short-term-memory-lstm-with-python/long-short-term-memory-lstm-with-python/

  Description: Bi-directional RNN that considers the context which comes after the target would be more accurate in prediction than the normal RNN. Bi-directional RNNs use two RNNs, one for forward direction processing and one for backward direction. The hidden states from both RNNs are then concatenated in a single hidden state.