Deep Learning: A Practitioner's Approach

By Joshua Patterson, Adam Gibson. 2017

539 citations recorded by the source corpus.

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Citations in the corpus

Forecasting Economics and Financial Time Series: ARIMA vs. LSTM (2018, background): “onding seasonal parameters P, D and Q. The parameter d is the order of difference frequency changing from non-stationary time series to stationary time series. 3.2 Long Short-Term Memory (LSTM) LSTM [Patterson, 2017] is a kind of Recurrent Neural Network (RNN) with the capability of remembering the ”
Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † (2018, background): “These gates in LSTM cell e ables it to preserve a more const nt erro th t can b back propagated through time layers llowing recurrent et to continue to learn over many time steps [38].”
A Review of Vibration-Based Damage Detection in Civil Structures: From Traditional Methods to Machine Learning and Deep Learning Applications (2020, methodology): “While this terminology is not wrong, based on the categorization of Patterson and Gibson [179], in this paper Fallahian et al.”
Deep Learning for Anomaly Detection: A Survey (2019, methodology): “Unsupervised DAD techniques are used in automatic labeling of unlabelled data samples since labeled data is very hard to obtain (Patterson and Gibson [2017]).”
A Deep Collocation Method for the Bending Analysis of Kirchhoff Plate (2021, background): “Several types of deep neural networks such as convolutional neural networks (CNN) and recurrent/recursive neural networks (RNN) [16] have been created, which further boost the application of deep learning in image processing [17], object detection [18], speech recognition [19] and many other…”
Deep Learning for Intelligent Wireless Networks: A Comprehensive Survey (2018, unknown): “, Deep Learning (DL) [1].”
Deep Convolutional Neural Networks Versus Multilayer Perceptron for Financial Prediction (2018, background): “Deep learning (DL) solves the problem of representation learning by the introduction of the representations that are expressed in terms of other, simpler representations [12], [13].”
Deep Autoencoder based Energy Method for the Bending, Vibration, and Buckling Analysis of Kirchhoff Plates (2020, methodology): “Various variants such as multilayer perceptron (MLP), convolutional neural networks (CNN) and recurrent/recursive neural networks (RNN) [19] have been developed and applied to e.”
A Survey of Deep Learning Techniques for Weed Detection from Images (2021, background): “On the other hand, Pearlstein et al. (2016) created complex occlusion of crops and weeds and generated variation in leaf size, colour, and orientation by producing synthetic data.”
A Comparison of ARIMA and LSTM in Forecasting Time Series (2018, background): “Long Short-Term Memory (LSTM) [17] is a kind of Recurrent Neural Network (RNN) with the capability of remembering the values from earlier stages for the purpose of future use.”
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture (2019, background): “As Patterson and Gibson [35] recommend, to preserve important features in the input data, dropout is disabled in the first layer, L1.”