DIMENSION reduction (Statistics)DEEP learningSTOCK pricesIn recent decades, increasing research attention has been directed toward predicting the price of stocks in financial markets using deep learning methods. For instance, recurrent neural network (RNN) is known to be compe...
Development in the field of computer-aided learning and testing have stimulated the progress of novel and efficient knowledge-based expert systems that have shown hopeful outcomes in a broad variety of practical applications. In particular, deep learning
compute xHat by projecting the xRot back onto the original axes%to see the effect of dimension reduction% --- YOUR CODE HERE ---k=1; % Use k =1and project the data onto the first eigenbasis xHat= zeros(size(x)); % You need to computethis...
本节是在学习UFLDL第二节和结合上节的博文:Deep Learning三:预处理之主成分分析与白化_总结(斯坦福大学UFLDL深度学习教程)的基础上练习的,练习内容是Exercise:PCA in 2D,主要是讲二维数据的PCA、PCA白化、ZCA白化,下一节讲二维自然图像的PCA、PCA白化、ZCA白化。 实验环境:win7 matlab2015b 一些matlab函数 彩色分...
The current development in deep learning is witnessing an exponential transition into automation applications. This automation transition can provide a pro
机器学习(Machine Learning)&深度学习(Deep Learning)资料(Chapter 1) 注:机器学习资料篇目一共500条,篇目二开始更新希望转载的朋友,你可以不用联系我.但是一定要保留原文链接,因为这个项目还在继续也在不定期更新.希望看到文章的朋友能够学到更多.此外:某些资料在中
DeepProg: an ensemble of deep-learning and machine-learning models for prognosis prediction using multi-omics data DeepProg:使用多组学数据的预后预测的深度学习和机器学习模型集成 01 文献速递介绍 大多数基于生存的分子签名都是基于单一类型的组学数据[1]。由于每种组学平台都有其特定的局限性和噪音,基于多组...
In the present paper,a algorithm based on information entropy is proposed to study the information loss of deep learning dimension reduction. Based on the research of stacked autoencoder,the Shannon information theory is applied to the information loss of deep learning dimension reduction,to calculate...
This paper introduces a novel high-dimensional data abstraction (HDDA) framework for dimension reduction in reliability analysis. It first involves training of a failure-informed autoencoder network to reduce the dimensionality of the high-dimensional input space, aiming at creating a distinguishable ...
In this blog post, we’ll look at the application of some deep learning techniques, usually used on image or text data, on non-time series tabular data in the decreasing level of conventionality. Autoencoders for Dimensionality Reduction Conventionally, autoencoders have been used ...