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Home > Data > Deep Learning > Neural Networks with R
Neural Networks with R
Neural Networks with R

Neural Networks with R: Build smart systems by implementing popular deep learning models in R

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Profile Icon Balaji Venkateswaran Profile Icon Giuseppe Ciaburro
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Neural Networks with R

Learning Process in Neural Networks

Just as there are many different types of learning and approaches to human learning, so we can say about the machines as well. To ensure that a machine will be able to learn from experience, it is important to define the best available methodologies depending on the specific job requirements. This often means choosing techniques that work for the present case and evaluating them from time to time, to determine if we need to try something new.

 We have seen the basics of neural networks in Chapter 1, Neural Network and Artificial Intelligence Concepts, and also two simple implementations using R. In this chapter, we will deal with the learning process, that is how to train, test, and deploy a neural network machine learning model. The training phase is used for learning, to fit the parameters of the neural networks. The testing phase is...

What is machine learning?

What do we mean by the term machine learning? The definition is quite difficult, to do so, we are asking large field of scientists to help. We can mention an artificial intelligence pioneer's quote:

"Field of study that gives computers the ability to learn without being explicitly programmed." 

 – Arthur Samuel

Machine learning is about training a model or an algorithm with data and then using the model to predict any new data. For example, a toddler is taught how to walk from his crawling phase. Initially, the toddler's parents hold the toddler's hand to help him up, and he is taught through the data that is given. On the basis of these procedures, if an obstacle presents itself in the toddler's path or if there is a turn somewhere, the toddler is able to navigate on his own after the training. The...

Supervised learning

Supervised learning is a learning method where there is a part of the training data which acts as a teacher to the algorithm to determine the model. The machine is taught what to learn from the target data. The target data, or dependent or response variables, are the outcome of the collective action of the independent variables. The network training is done with the target data and its behavior with patterns of input data. The target labels are known in advance and the data is fed to the algorithm to derive the model.

Most of neural network usage is done using supervised learning. The weights and biases are adjusted based on the output values. The output can be categorical (like true/false or 0/1/2) or continuous (like 1,2,3, and so on). The model is dependent on the type of output variables, and in the case of neural networks, the output layer is built on...

Unsupervised learning

In unsupervised learning (or self organization), the output layer is trained to organize the input data into another set of data without the need of a target variable. The input data is analyzed and patterns are found in it to derive the output, as shown in the following figure. Since there is no teacher (or target variable), this type of learning is called unsupervised learning.

The different techniques available for unsupervised learning are as follows:

  • Clustering (K-means, hierarchical)
  • Association techniques
  • Dimensionality reduction
  • Self Organizing Map (SOM)/ Kohonen networks

To summarize, the two main types of machine learning are depicted in the following figure:

For neural networks, we have both the types available, using different ways available in R.

Reinforcement learning

Reinforcement learning is a type of machine learning where there is constant feedback given to the model to adapt to the environment. There is a performance evaluation at each step to improve the model. For neural networks, there is a special type called Q-learning, combined with neuron to implement reinforcement learning in the backpropagation feedback mechanism. The details are out of scope of this book.

The following are the three types of learnings we have covered so far:

Training and testing the model

Training and testing the model forms the basis for further usage of the model for prediction in predictive analytics. Given a dataset of 100 rows of data, which includes the predictor and response variables, we split the dataset into a convenient ratio (say 70:30) and allocate 70 rows for training and 30 rows for testing. The rows are selected in random to reduce bias.

Once the training data is available, the data is fed to the neural network to get the massive universal function in place. The training data determines the weights, biases, and activation functions to be used to get to output from input. Until recently, we could not say that a weight has a positive or a negative influence on the target variable. But now we've been able to shed some light inside the black box. For example, by plotting a trained neural network, we can discover trained...

What is machine learning?

What do we mean by the term machine learning? The definition is quite difficult, to do so, we are asking large field of scientists to help. We can mention an artificial intelligence pioneer's quote:

"Field of study that gives computers the ability to learn without being explicitly programmed." 

 – Arthur Samuel

Machine learning is about training a model or an algorithm with data and then using the model to predict any new data. For example, a toddler is taught how to walk from his crawling phase. Initially, the toddler's parents hold the toddler's hand to help him up, and he is taught through the data that is given. On the basis of these procedures, if an obstacle presents itself in the toddler's path or if there is a turn somewhere, the toddler is able to navigate on his own after the training. The data used for training is the training data and the recipient continues to learn even after the formal training.

Machines too can be taught like toddlers to do a task based...

Supervised learning

Supervised learning is a learning method where there is a part of the training data which acts as a teacher to the algorithm to determine the model. The machine is taught what to learn from the target data. The target data, or dependent or response variables, are the outcome of the collective action of the independent variables. The network training is done with the target data and its behavior with patterns of input data. The target labels are known in advance and the data is fed to the algorithm to derive the model.

Most of neural network usage is done using supervised learning. The weights and biases are adjusted based on the output values. The output can be categorical (like true/false or 0/1/2) or continuous (like 1,2,3, and so on). The model is dependent on the type of output variables, and in the case of neural networks, the output layer is built on the type of target variable.

Note

For neural networks, all the independent and dependent variables need to be numeric...

Unsupervised learning

In unsupervised learning (or self organization), the output layer is trained to organize the input data into another set of data without the need of a target variable. The input data is analyzed and patterns are found in it to derive the output, as shown in the following figure. Since there is no teacher (or target variable), this type of learning is called unsupervised learning.

The different techniques available for unsupervised learning are as follows:

  • Clustering (K-means, hierarchical)
  • Association techniques
  • Dimensionality reduction
  • Self Organizing Map (SOM)/ Kohonen networks

To summarize, the two main types of machine learning are depicted in the following figure:

For neural networks, we have both the types available, using different ways available in R.

Reinforcement learning

Reinforcement learning is a type of machine learning where there is constant feedback given to the model to adapt to the environment. There is a performance evaluation at each step to improve the model. For neural networks, there is a special type called Q-learning, combined with neuron to implement reinforcement learning in the backpropagation feedback mechanism. The details are out of scope of this book.

The following are the three types of learnings we have covered so far:

Training and testing the model

Training and testing the model forms the basis for further usage of the model for prediction in predictive analytics. Given a dataset of 100 rows of data, which includes the predictor and response variables, we split the dataset into a convenient ratio (say 70:30) and allocate 70 rows for training and 30 rows for testing. The rows are selected in random to reduce bias.

Once the training data is available, the data is fed to the neural network to get the massive universal function in place. The training data determines the weights, biases, and activation functions to be used to get to output from input. Until recently, we could not say that a weight has a positive or a negative influence on the target variable. But now we've been able to shed some light inside the black box. For example, by plotting a trained neural network, we can discover trained synaptic weights and basic information about the training process.

Once the sufficient convergence is achieved, the...

The data cycle

The data forms a key component for model building and the learning process. The data needs to be collected, cleaned, converted, and then fed to the model for learning. The overall data life cycle is shown as follows:

One of the critical requirements for modeling is having good and balanced data. This helps in higher accuracy models and better usage of the available algorithms. A data scientist's time is mostly spent on cleansing the data before building the model.

We have seen the training and testing before deployment of the model. For testing, the results are captured as evaluation metrics, which helps us decide if we should use a particular model or change it instead.

We will see the evaluation metrics next.

Evaluation metrics

Evaluating a model involves checking if the predicted value is equal to the actual value during the testing phase. There are various metrics available to check the model, and they depend on the state of the target variable.

For a binary classification problem, the predicted target variable and the actual target variable can be in any of the following four states:

Predicted

Actual

Predicted = TRUE

Actual = TRUE

Predicted = TRUE

Actual = FALSE

Predicted = FALSE

Actual = TRUE

Predicted = FALSE

Actual = FALSE

 

When we have the predicted and actual values as same values, we are said to be accurate. If all predicted and actual values are same (either all TRUE or all FALSE), the model is 100 percent accurate. But, this is never the case.

Since neural networks are approximation models, there is always a bit of error possible. All the four states mentioned in the previous table are possible.

We define the following terminology and metrics for a model:

  • True Positives (TP): All cases where the...

Learning in neural networks

As we saw in Chapter 1, Neural Network and Artificial Intelligence Concepts, neural networks is a machine learning algorithm that has the ability to learn from data and give us predictions using the model built. It is a universal function approximation, that is, any input, output data can be approximated to a mathematical function. 

The forward propagation gives us an initial mathematical function to arrive at output(s) based on inputs by choosing random weights. The difference between the actual and predicted is called the error term. The learning process in a feed-forward neural network actually happens during the backpropagation stage. The model is fine tuned with the weights by reducing the error term in each iteration. Gradient descent is used in the backpropagation process.

Let us cover the backpropagation in detail in this chapter, as it is an important machine learning aspect for neural networks.

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Key benefits

  • Develop a strong background in neural networks with R, to implement them in your applications
  • Build smart systems using the power of deep learning
  • Real-world case studies to illustrate the power of neural network models

Description

Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning. This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you’ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases. By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.

Who is this book for?

This book is intended for anyone who has a statistical background with knowledge in R and wants to work with neural networks to get better results from complex data. If you are interested in artificial intelligence and deep learning and you want to level up, then this book is what you need!

What you will learn

  • Set up R packages for neural networks and deep learning
  • Understand the core concepts of artificial neural networks
  • Understand neurons, perceptrons, bias, weights, and activation functions
  • Implement supervised and unsupervised machine learning in R for neural networks
  • Predict and classify data automatically using neural networks
  • Evaluate and fine-tune the models you build.
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Publication date : Sep 27, 2017
Length: 270 pages
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Language : English
ISBN-13 : 9781788397872
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Concepts :
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ISBN-13 : 9781788397872
Category :
Data
Languages :
R
Concepts :
Deep Learning

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Table of Contents

7 Chapters
Neural Network and Artificial Intelligence Concepts Chevron down icon Chevron up icon
Neural Network and Artificial Intelligence Concepts
Introduction
Inspiration for neural networks
How do neural networks work?
Layered approach
Weights and biases
Training neural networks
Epoch
Activation functions
Different activation functions
Which activation functions to use?
Perceptron and multilayer architectures
Forward and backpropagation
Step-by-step illustration of a neuralnet and an activation function
Feed-forward and feedback networks
Gradient descent
Taxonomy of neural networks
Simple example using R neural net library - neuralnet()
Implementation using nnet() library
Deep learning
Pros and cons of neural networks
Best practices in neural network implementations
Quick note on GPU processing
Summary
Learning Process in Neural Networks Chevron down icon Chevron up icon
Learning Process in Neural Networks
What is machine learning?
Supervised learning
Unsupervised learning
Reinforcement learning
Training and testing the model
The data cycle
Evaluation metrics
Learning in neural networks
Back to backpropagation
Neural network learning algorithm optimization
Supervised learning in neural networks
Unsupervised learning in neural networks
Summary
Deep Learning Using Multilayer Neural Networks Chevron down icon Chevron up icon
Deep Learning Using Multilayer Neural Networks
Introduction of DNNs
R for DNNs
Multilayer neural networks with neuralnet
Training and modeling a DNN using H2O
Deep autoencoders using H2O
Summary
Perceptron Neural Network Modeling – Basic Models Chevron down icon Chevron up icon
Perceptron Neural Network Modeling – Basic Models
Perceptrons and their applications
Simple perceptron – a linear separable classifier
Linear separation
The perceptron function in R
Multi-Layer Perceptron
MLP R implementation using RSNNS
Summary
Training and Visualizing a Neural Network in R Chevron down icon Chevron up icon
Training and Visualizing a Neural Network in R
Data fitting with neural network
Classifing breast cancer with a neural network
Early stopping in neural network training
Avoiding overfitting in the model
Generalization of neural networks
Scaling of data in neural network models
Ensemble predictions using neural networks
Summary
Recurrent and Convolutional Neural Networks Chevron down icon Chevron up icon
Recurrent and Convolutional Neural Networks
Recurrent Neural Network
The rnn package in R
LSTM model
Convolutional Neural Networks
Common CNN architecture - LeNet
Humidity forecast using RNN
Summary
Use Cases of Neural Networks – Advanced Topics Chevron down icon Chevron up icon
Use Cases of Neural Networks – Advanced Topics
TensorFlow integration with R
Keras integration with R
MNIST HWR using R
LSTM using the iris dataset
Working with autoencoders
PCA using H2O
Autoencoders using H2O
Breast cancer detection using darch
Summary

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No single book today does as good a job as this one in blending the right amount of theory with real-life examples and coverage of all the current useful algorithms
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With the help of R, this book helped me understand A.I. In a steady and reasonable progression. Thank you much.
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About the authors

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Profile icon Balaji Venkateswaran
Balaji Venkateswaran
Balaji Venkateswaran is an AI expert, data scientist, machine learning practitioner, and database architect. He has 17+ years of experience in investment banking payment processing, telecom billing, and project management. He has worked for major companies such as ADP, Goldman Sachs, MasterCard, and Wipro. Balaji is a trainer in data science, Hadoop, and Tableau. He holds a postgraduate degree PG in business analytics from Great Lakes Institute of Management, Chennai. Balaji has expertise relating to statistics, classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures. His main interests are neural networks and deep learning. Balaji holds various certifications in IBM SPSS, IBM Watson, IBM big data architect, cloud architect, CEH, Splunk, Salesforce, Agile CSM, and AWS. If you have any questions, don't hesitate to message him on LinkedIn (balvenkateswaran); he will be more than glad to help fellow data scientists.
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Profile icon Giuseppe Ciaburro
Giuseppe Ciaburro
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Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).
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Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela