Deep Learning Regression with R

Learn deep learning regression through a practical course with R statistical software using S&P 500® Index ETF prices historical data for algorithm learning. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business forecasting research. All of this while exploring the wisdom of best academics and practitioners in the field. 

Become a Deep Learning Regression Expert in this Practical Course with R

• 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.

Become a Deep Learning Regression Expert and Put Your Knowledge in Practice

Learning deep learning regression is indispensable for data mining applications in areas such as consumer analytics, finance, banking, health care, science, e-commerce and social media. It is also essential for academic careers in data mining, applied statistical learning or artificial intelligence. And its necessary for business forecasting research.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for algorithm learning to achieve greater effectiveness. 

Content and Overview

This practical course contains 33 lectures and 4 hours of content. It’s designed for all deep learning regression knowledge levels and a basic understanding of R statistical software is useful but not required.

At first, you’ll learn how to read or download S&P 500® Index ETF prices historical data to perform deep learning regression operations by installing related packages and running script code on RStudio IDE.

Then, you’ll define algorithm features by creating target and predictor variables for supervised regression learning task. Next, you’ll only include relevant predictor features subset or transformations in algorithm learning through features selection and features extraction procedures. For features selection, you’ll implement Student t-test and ANOVA F-test univariate filter methods. For features extraction, you’ll implement principal components analysis. After that, you’ll define algorithm training through mapping optimal relationship between target and predictor features within training range. For algorithm training, you’ll define optimal parameters estimation or fine tuning, bias-variance trade-off, optimal model complexity and artificial neural network regularization. For artificial neural network regularization, you’ll define node connection weights, visible and hidden layers dropout fractions, stochastic gradient descent algorithm learning and momentum rates. Later, you’ll define algorithm testing through evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics. For scale-dependent metrics, you’ll define mean absolute error and root mean squared error. For scale-independent metrics, you’ll define mean absolute percentage error and mean absolute scaled error.

Next, you’ll define artificial neural network. Then, you’ll implement algorithm training for mapping optimal relationship between target and predictor features. For algorithm training, you’ll use only relevant predictor features subset or transformations through principal components analysis procedure and nodes connections weight decay regularization. After that, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics.

After that, you’ll define deep neural network. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features. For algorithm training, you’ll use only relevant features subset or transformations and visible or hidden dropout fractions regularization. For features extraction, you’ll use principal components analysis, stacked autoencoders, restricted Boltzmann machines and deep belief network. Later, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics.

Later, you’ll define recurrent neural network and long short-term memory. Next, you’ll implement algorithm training for mapping optimal relationship between target and predictor features. For algorithm training, you’ll use stochastic gradient descent algorithm learning rate regularization. Then, you’ll implement algorithm testing for evaluating previously optimized relationship forecasting accuracy through scale-dependent and scale-independent metrics. Finally, you’ll compare deep learning regression algorithms training and testing.