Deep Learning Regression with R
3.3 (12 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
144 students enrolled

Deep Learning Regression with R

Learn deep learning regression from basic to expert level through a practical course with R statistical software.
3.4 (12 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
144 students enrolled
Created by Diego Fernandez
Last updated 1/2018
English
English [Auto]
Current price: $16.99 Original price: $24.99 Discount: 32% off
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This course includes
  • 4 hours on-demand video
  • 7 articles
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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.
Course content
Expand all 33 lectures 03:47:04
+ Course Overview
7 lectures 27:30

In this lecture you will view course disclaimer and learn which are its objectives, how you will benefit from it, its previous requirements and my profile as instructor.

Preview 04:47

In this lecture you will learn that it is recommended to view course in an ascendant manner as each section builds on last one and also does its complexity. You will also study course structure and main sections (algorithm learning, artificial neural network, deep neural network and recurrent neural network).

Preview 03:42

In this lecture you will learn deep learning regression definition, R statistical software and RStudio Integrated Development Environment (IDE) downloading websites.

Deep Learning Regression
05:13

In this lecture you will learn deep learning regression data reading or downloading into RStudio Integrated Development Environment (IDE), data sources, R script code files originally in .TXT format that need to be converted in .R format with pairs trading analysis computation instructions, R packages installation (caret, deepnet, forecast, neuralnet, nnet, quantmod, rnn, tseries) and related code (library(), read.csv(), xts(), getSymbols() functions).

Deep Learning Regression Data
13:38

Before starting course please download .TXT data file in .CSV format as additional resources.

Course Data File
00:03

Before starting course please download .TXT R script code file as additional resources.

Course Script Code File
00:03

You can download .PDF section slides file as additional resources.

Course Overview Slides
00:02
+ Algorithm Learning
7 lectures 37:52

You can download .PDF section slides file as additional resources.

Algorithm Learning Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to algorithm learning.

Preview 04:45

In this lecture you will learn algorithm features definition and main calculations (dailyReturn(), Lag(), cbind(), colnames(), complete.cases(), length() functions).

Algorithm Features
06:17

In this lecture you will learn features selection definition and main calculations (lm(), summary() functions).

Features Selection
09:05

In this lecture you will learn features extraction definition and main calculations (princomp(), summary(), plot() functions).

Features Extraction
08:07

In this lecture you will algorithm training definition.

Algorithm Training
06:24

In this lecture you will learn algorithm testing definition.

Algorithm Testing
03:12
+ Artificial Neural Network
4 lectures 27:08

You can download .PDF section slides file as additional resources.

Artificial Neural Network Slides
00:02

In this lecture you will learn section lectures’ details and main themes to be covered related to artificial neural network.

Artificial Neural Network Overview
05:21

In this lecture you will learn artificial neural network training definition and main calculations (Sys.time(), train(), $bestTune, plot() functions). 

Artificial Neural Network Training
10:21

In this lecture you will learn artificial neural network testing definition and main calculations (predict.train(), xts(), as.data.frame(), as.Date(), plot(), lines(), ts(), accuracy() functions).

Artificial Neural Network Testing
11:24
+ Deep Neural Network
8 lectures 01:10:25

You can download .PDF section slides file as additional resources.

Deep Neural Network Slides
00:02

In this lecture you will learn section lectures’ detail and main themes to be covered related to deep neural network.

Deep Neural Network Overview
06:27

In this lecture you will learn deep neural network training definition and main calculations (Sys.time(), train(), $bestTune, plot() functions). 

Deep Neural Network Training
09:49

In this lecture you will learn deep neural network testing definition and main calculations (predict.train(), xts(), as.data.frame(), as.Date(), plot(), lines(), ts(), accuracy() functions). 

Deep Neural Network Testing
09:52

In this lecture you will learn stacked autoencoder deep neural network training definition and main calculations (Sys.time(), train(), $bestTune, $results functions). 

Stacked Autoencoder Training
09:15

In this lecture you will learn stacked autoencoder deep neural network testing definition and main calculations (predict.train(), xts(), as.data.frame(), as.Date(), plot(), lines(), ts(), accuracy() functions).

Stacked Autoencoder Testing
06:38

In this lecture you will learn deep belief network deep neural network training definition and main calculations (min(), max(), nrow(), Sys.time(), expand.grid(), cbind(), colnames(), sqrt(), mean(), na.omit(), dbn.dnn.train(), which()  functions, for(){} loop, ifelse() conditional).

Deep Belief Network Training
18:10

In this lecture you will learn deep belief network deep neural network testing definition and main calculations (nn.predict(), max(), min(), xts(), as.data.frame(), as.Date(), plot(), lines(), ts(), accuracy() functions).

Deep Belief Network Testing
10:12
+ Recurrent Neural Network
7 lectures 01:04:06

You can download .PDF section slides file as additional resources.

Recurrent Neural Network Slides
00:02

In this lecture you will learn section lectures’ detail and main themes to be covered related to recurrent neural network.

Recurrent Neural Network Overview
09:12

In this lecture you will learn recurrent neural network training definition and main calculations (matrix(), coredata(), nrow(), sample(), Sys.time(), expand.grid(), cbind(), colnames(), min(), trainr(), $error, which(), plot(), colMeans()  functions, for(){} loop, ifelse() conditional).

Recurrent Neural Network Training
13:28

In this lecture you will learn recurrent network testing definition and main calculations (predictr(), as.vector(), max(), min(), xts(), as.data.frame(), as.Date(), plot(), lines(), ts(), accuracy() functions).

Recurrent Neural Network Testing
07:26
Long Short-Term Memory Training
14:27

In this lecture you will learn long short-term memory recurrent network testing definition and main calculations (predictr(), as.vector(), max(), min(), xts(), as.data.frame(), as.Date(), plot(), lines(), ts(), accuracy() functions).

Long Short-Term Memory Testing
09:28

In this lecture you will learn deep learning regression comparison definition and main calculations (accuracy() function).

Deep Learning Regression Comparison
10:03
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.
Description

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.

Who this course is for:
  • Undergraduates or postgraduates who want to learn about deep learning regression using R statistical software.
  • Academic researchers who wish to deepen their knowledge in data mining, applied statistical learning or artificial intelligence.
  • Business data scientist who desires to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.