[ FreeCourseWeb.com ] Deep Learning Regression with R
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h264, yuv420p, 1280x720 |ENGLISH, aac, 48000 Hz, 2 channels | 3h 47 mn | 573.28 MB
Created by: Diego Fernandez
Learn deep learning regression from basic to expert level through a practical course with R statistical software.
What you'll learn
Read or download S&P 500® Index ETF prices data and perform deep learning regression operations by installing related packages and running script code on RStudio IDE.
Create target and predictor algorithm features for supervised regression learning task.
Select relevant predictor features subset through Student t-test and ANOVA F-test univariate filter methods and extract predictor features transformations through principal component analysis.
Train algorithm for mapping optimal relationship between target and predictor features through artificial neural network, deep neural network and recurrent neural network.
Regularize algorithm learning through nodes connections weight decay, visible or hidden layers dropout fractions and stochastic gradient descent algorithm learning rate.
Extract algorithm predictor features through stacked autoencoders, restricted Boltzmann machines and deep belief network.
Minimize recurrent neural network vanishing gradient problem through long short-term memory units.
Test algorithm for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics.
Assess mean absolute error, root mean squared error for scale-dependent metrics and mean absolute percentage error, mean absolute scaled error for scale-independent metrics.
Requirements
R statistical software is required. Downloading instructions included.
RStudio Integrated Development Environment (IDE) is recommended. Downloading instructions included.
Practical example data and R script code files provided with the course.
Prior basic R statistical software knowledge is useful but not required.
Mathematical formulae kept at minimum essential level for main concepts understanding.
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