原文链接:https://towardsdatascience.com/deep-learning-no-lstms-are-not-dead-20217553b87a 觉得不错,请点个在看呀
Deep learning (DL) is a branch of machine learning based on a set of algorithms that attempts to model high-level abstractions in data by using artificial neural network (ANN) architectures composed of multiple non-linear transformations. One of the more popular DL deep neural networks is the ...
importnumpyasnpimporttorchdefcreate_sequences(data,seq_length):xs,ys=[],[]foriinrange(len(data...
Y_train = create_sliding_windows(train_data_scaler, window_size) X_test, Y_test = create_slid...
https://towardsdatascience.com/reading-between-the-layers-lstm-network-7956ad192e58 构建深度神经网络最关键的部分之一是——当数据流经不同的层时,要对其有一个清晰的视图,这些层经历了维度的变化、形状的改变、扁平化和重新塑造…… LSTM Network Architecture for Sentiment Analysis ...
, data science is very useful for visualization data and our proposed method using Long Short-Term Memory (LSTM) can be used as predictor in short term data with accuracy 94.57% comes from the short term (1 year) with high epoch in training phase rather than using 3 years training data....
选自TowardsDataScience 作者:Nimesh Sinha 机器之心编译 参与:Nurhachu Null、路雪 本文对 LSTM 进行了简单介绍,并讲述了如何使用 LSTM 在 Keras 中快速实现情感分析任务。 长短期记忆网络通常被称为 LSTM,它是由 Hochreiter 和 Schmiduber 提出的,被广泛地应用在语音识别、语言建模、情感分析和文本预测中。在深入...
来源| Towards Data Science 你是否想知道LSTM层学到了什么?有没有想过是否有可能看到每个单元如何对最终输出做出贡献。我很好奇,试图将其可视化。在满足我好奇的神经元的同时,我偶然发现了Andrej Karpathy的博客,名为“循环神经网络的不合理有效性”。如果你想获得更深入的解释,建议你浏览他的博客。
Anomaly Detection with LSTM in Kera[https://towardsdatascience.com/anomaly-detection-with-lstm-in-keras-8d8d7e50ab1b] 原文GitHub地址[https://github.com/cerlymarco/MEDIUM_NoteBook/tree/master/Anomaly_Detection_LSTM] In [1] import numpy as np import pandas as pd import matplotlib.pyplot as pl...
作者|Praneet Bomma 编译|VK 来源|https://towardsdatascience.com/visualising-lstm-activations-in-keras-b50206da96ff 你是否想知道LSTM层学到了什么?有没有想过是否有可能看到每个单元如何对最终输出做出贡献。我很好奇,试图将其可视化。在满足我好奇的神经元的同时,我偶然发现了Andrej Karpathy的博客,名为“循...