摘要在本文中,作者将通用深度学习 (DL) 框架应用于答案选择任务,该框架不依赖于手动定义的特征或语言工具。基本框架是基于双向长短期记忆(biLSTM)模型构建问题和答案的嵌入,并通过余弦相似度测量它们的接近度…
【3】:《Urban Water Supply Forecasting Based on CNN-LSTM-AM Spatiotemporal Deep Learning Model》 该文献提出了一种时空深度学习模型 CNN-LSTM-AM,用于城市日供水量的预测。创新点如下:将 CNN 和 LSTM 两种深度学习模型结合,构建了一个时空深度学习框架,充分利用 CNN 在空间特征提取和 LSTM 在时间特征挖掘方面...
A method to create X and Y matrix from a time series array for the training of deep learning models """ # Extracting the number of features that are passed from the array n_features = ts.shape[1] # Creating placeholder lists X, Y = [], [] if len(ts) - lag <= 0: X.append(...
Predicting healthcare trajectories from medical records: A deep learning approach(从医疗记录预测医疗保健轨迹:深度学习的方法LSTM神经网络) 摘要 个性化预测医学需要对患者疾病和护理过程进行建模,该过程本身具有长期的时间依赖性。储存在电子医疗记录中的医疗观察是零散的,不规则的。我们介绍DeepCare(一种端到端的深层...
A method to create X and Y matrix from a time series array for the training of deep learning models """ # Extracting the number of features that are passed from the array n_features = ts.shape[1] # Creating placeholder lists X, Y = [], [] ...
Deep Learning基础--理解LSTM网络 循环神经网络(RNN) 人们的每次思考并不都是从零开始的。比如说你在阅读这篇文章时,你基于对前面的文字的理解来理解你目前阅读到的文字,而不是每读到一个文字时,都抛弃掉前面的思考,从头开始。你的记忆是有持久性的。
Already in 2010, we introduced our deep and fast GPU-based NNs to Arcelor Mittal, the world's largest steel maker, and were able to greatly improve steel defect detection through CNNs[ST](beforeImageNet 2012).This may have been the first Deep Learning breakthrough in heavy industry,and hel...
Recently, deep learning has been successfully applied to network security assessments and intrusion detection systems (IDSs) with various breakthroughs such as using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) to classify malicious traffic. However, these state-of-the-art sys...
A Deep Learning Approach Combining CNN and Bi-LSTM with SVM Classifier for Arabic Sentiment Analysis In this paper, we employ a deep learning model with modified architecture that combines Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) for feature extraction, ...
再回首DeepLearning遇见了LSTM和GRU,这篇文章基于前面的重温循环神经网络(RNN), 通过前面的分析, 我们已经知道了RNN中的梯度消失和爆炸现在究竟是怎么回事并且也知道了引起梯度消失和爆炸的原因, 而又由于梯度消失, 导致了RNN并不擅长捕捉序列的长期关联, 所以基于这两个问题, 导致现在RNN使用的并不是太多, 而是使用...