Advanced Neural Networks in R - A Practical Approach

Boost your data science skills - learn to build and train complex neural network using the R program

3.95 (27 reviews)
Udemy
platform
English
language
Data & Analytics
category
Advanced Neural Networks in R - A Practical Approach
12,584
students
3 hours
content
Dec 2020
last update
$19.99
regular price

What you will learn

Create multilayer perceptrons and use them for predictions

Build and train probabilistic neural networks

Build and train generalized regression neural networks

Build and train recurrent neural networks

Use recurrent neural networks for time series forecasting

Why take this course?

Neural networks are powerful predictive tools that can be used for almost any machine learning problem with very good results. If you want to break into deep learning and artificial intelligence, learning neural networks is the first crucial step.


This is why I’m inviting you to an exciting journey through the world of complex, state-of-the-art neural networks. In this course you will develop a strong understanding of the most utilized neural networks, suitable for both classification and regression problems.


The mathematics behind neural networks is particularly complex, but you don’t need to be a mathematician to take this course and fully benefit from it. We will not dive into complicated maths - our emphasis here is on practice. You will learn how to operate neural networks using the R program, how to build and train models and how to make predictions on new data.


All the procedures are explained live, on real life data sets. So you will advance fast and be able to apply your knowledge immediately.


This course contains four comprehensive sections.


1. Multilayer Perceptrons – Beyond the Basics


Learn to use multilayer perceptrons to make predictions for both categorical and continuous variables. Moreover, learn how to test your models accuracy using the k-fold cross-validation technique and how improve predictions by manipulating various parameters of the network.


3. Generalized Regression Neural Networks


If you have to solve a regression problem (where your response variable is numeric), these networks can be very effective. We’ll show how to predict a car value based on its technical characteristics and how to improve the prediction by controlling the smoothing parameter of our model. The k-fold cross-validation techniques will also be employed to identify better models.


4. Recurrent Neural Networks


These networks are useful for many prediction problems, but they are particularly valuable for time series modelling and forecasting. In this course we focus on two types of recurrent neural networks: Elman and Jordan. We are going to use them to predict future air temperatures based on historical data. Making truthful predictions on time series is generally very tough, but we will do our best to build good quality models and get satisfactory values for the prediction accuracy metrics.


For each type of network, the presentation is structured as follows:


  • a short, easy to understand theoretical introduction (without complex mathematics)

  • how to train the network in R

  • how to test the network to make sure that it does a good prediction job on independent data sets.


For every neural network, a number of practical exercises are proposed. By doing these exercises you’ll actually apply in practice what you have learned.


This course is your opportunity to become a neural network expert in a few days only (literally). With my video lectures, you will find it very easy to master these major neural network and build them in R. Everything is shown live, step by step, so you can replicate any procedure at any time you need it.


So click the “Enrol” button to get instant access to your course. It will surely get you some new, valuable skills. And, who knows, it could greatly enhance your future career.


See you inside!


Content

Getting Started

Introduction

Multilayer Perceptron - Beyond the Basics

What Are Multilayer Perceptrons?
How Multilayer Perceptrons Work?
How Does a Multilayer Perceptron Learn?
Prediction Accuracy Metrics
ROC Curve
Using MLPs With Categorical Response Variables: Building the Network
Using MLPs With Categorical Response Variables: Making Predictions
Using MLPs With Categorical Response Variables: ROC Curve
Using MLPs With Categorical Response Variables: Playing With the Hidden Nodes
Using MLPs With Categorical Response Variables: K-Fold Validation
Using MLPs With Continuous Response Variables: Building the Network
Using MLPs With Continuous Response Variables: Making Predictions
Using MLPs With Continuous Response Variables: Manipulating the Hidden Nodes
Using MLPs With Continuous Response Variables: K-Fold Validation

Probabilistic Neural Networks

What Are Probablistic Neural Networks?
Data Preparation
Building the Network
Making Predictions
Finding the Optimal Sigma
Validating Our Model

Generalized Regression Neural Networks

What Are Generalized Regression Neural Networks?
Data Preparation
Building the Network
Making Predictions
Finding the Optimal Sigma

Recurrent Neural Networks

What Are Recurrent Neural Networks?
Measuring the Predictive Performance
Elman Networks: Data Preparation
Elman Networks: Building the Model
Elman Networks: Making Predictions
Elman Networks: Adding More Predictors
Elman Networks: Making Predictions With Our New Model
Jordan Networks: Data Preparation
Jordan Networks: Building the Model
Jordan Networks: Making Predictions

Practice

Data Sets Description
Practical Exercises

Useful Links

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3653880
udemy ID
11/23/2020
course created date
12/9/2020
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