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Home > Data > Deep Learning > Hands-On Deep Learning with R
Hands-On Deep Learning with R
Hands-On Deep Learning with R

Hands-On Deep Learning with R: A practical guide to designing, building, and improving neural network models using R

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Hands-On Deep Learning with R

Machine Learning Basics

Welcome to Hands-On Deep Learning with R! This book will take you through all of the steps that are necessary to code deep learning models using the R statistical programming language. It begins with simple examples as the first step for those just getting started, along with a review of the foundational elements of deep learning for those with more experience. As you progress through this book, you will learn how to code increasingly complex deep learning solutions for a wide variety of tasks. However, regardless of the complexity, each chapter will carefully detail each step. This is so that all topics and concepts can be fully comprehended and the reason for every line of code is completely explained.

In this chapter, we will go through a quick overview of the machine learning process as it will form a base for the subsequent chapters of this book.&...

An overview of machine learning

All deep learning is machine learning, but not all machine learning is deep learning. Throughout this book, we will focus on processes and techniques that are specific to deep learning in R. However, all the core principles of machine learning are essential to understand before we can move on to explore deep learning.

Deep learning is marked as a special subset of machine learning based on the use of neural networks that mimic brain activity behavior. The learning is referred to as being deep because, during the modeling process, the data is manipulated by a number of hidden layers. In this type of modeling, specific information is gathered from each layer. For example, one layer may find the edges of images while another finds particular hues.

Notable applications for this type of machine learning include the following:

  • Image recognition...

Preparing data for modeling

One of the benefits of deep learning is that it largely removes the need for feature engineering, which you may be used to with machine learning. That being said, the data still needs to be prepared before we begin modeling. Let's review the following goals to prepare data for modeling:

  • Remove no-information and extremely low-information variables
  • Identify dates and extract date parts
  • Handle missing values
  • Handle outliers

In this chapter, we will be investigating air quality data using data provided by the London Air Quality Network. Specifically, we will look at readings for nitrogen dioxide in the area of Tower Hamlets (Mile End Road) during 2018. This is a very small dataset with only a few features and approximately 35,000 observations. We are using a limited dataset here so that all of our code, even our modeling, runs quickly. That said...

Training a model on prepared data

Now that the data is ready, we will split it into train and test sets and run a simple model. The objective at this point is not to try to achieve the best performance, but rather to get some type of a benchmark result to use in the future as we try to improve our model.

Train and test data

When we build predictive models, we need to create two separate sets of data with the help of the following segments. One is used by the model to learn the task and the other is used to test how well the model learned the task. Here are the types of data that we will look at:

  • Train data: The segment of the data used to fit the model. The model has access to the explainer variables or independent variables...

Evaluating model results

We only know whether a model is successful if we can measure it, and it is worthwhile taking a moment to remember which metrics to use in which scenarios. Take, for example, a credit card fraud dataset where there is a large imbalance in the target variable because there will only be a, relatively, few cases of fraud among many non-fraudulent cases.

If we use a metric that just measures the percentage of the target variable that we predict successfully, then we will not be evaluating our model in a very helpful way. In this case, to keep the math simple, let's imagine we have 10,000 cases and only 10 of them are fraudulent accounts. If we predict that all cases are not fraudulent, then we will have 99.9% accuracy. This is very accurate, but it is not very helpful. Here is a review of the different metrics and when to use them.

...

Improving model results

Since we have a regression problem, we now know why we chose RMSE, and we have a baseline metric of performance, we can begin to work on improving our model. Every model will have its own different way of improving results; however, we can generalize slightly. Feature engineering helps to improve model performance; however, since this type of work is less important with deep learning, we will not focus on that here. Also, we have already used feature engineering to generate our date and time parts. In addition, we can run our model for longer at a slower learning rate and we can tune hyperparameters. In order to find the best values using this type of model improvement method, we will use a technique called grid search to look at a range of values for a number of different fields.

Let's search for the optimal number of rounds. Using the cross-validation...

Reviewing different algorithms

We have raced through machine learning relatively quickly, as we wanted to focus on the underlying concepts that will follow along with us as we head into deep learning. As such, we cannot offer a comprehensive explanation of all machine learning techniques; however, we will quickly review the different algorithm types here, as this will be helpful to remember going forward.

We'll do a quick review of the following machine learning algorithms:

  • Decision Trees: A decision tree is a simple model that makes up the base learners of many more complex algorithms. A decision tree simply splits a dataset at a given variable and notes the proportion of the target class that exists in the splits. For example, if we were to predict who is more likely to enjoy playing with baby toys, then a split on age would likely show that the split of the data...

Summary

In this chapter, we referred to a raw dataset, explored the data, and took the necessary preprocessing steps to get the data ready for modeling. We performed data type transformations to convert numbers and dates being stored as character strings into numeric and date value columns, respectively. In addition, we performed some feature engineering by breaking up the date value into its component parts. After completing preprocessing, we modeled our data. We followed an approach that included creating a baseline model and then tuning hyperparameters to improve our initial score. We used early stopping rounds and grid searches to identify hyperparameter values that produced the best results. After modifying our model-based results from our tuning procedures, we noticed much better performance.

All of the aspects of machine learning that were discussed in this chapter will...

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Key benefits

  • Understand deep learning algorithms and architectures using R and determine which algorithm is best suited for a specific problem
  • Improve models using parameter tuning, feature engineering, and ensembling
  • Apply advanced neural network models such as deep autoencoders and generative adversarial networks (GANs) across different domains

Description

Deep learning enables efficient and accurate learning from a massive amount of data. This book will help you overcome a number of challenges using various deep learning algorithms and architectures with R programming. This book starts with a brief overview of machine learning and deep learning and how to build your first neural network. You’ll understand the architecture of various deep learning algorithms and their applicable fields, learn how to build deep learning models, optimize hyperparameters, and evaluate model performance. Various deep learning applications in image processing, natural language processing (NLP), recommendation systems, and predictive analytics will also be covered. Later chapters will show you how to tackle recognition problems such as image recognition and signal detection, programmatically summarize documents, conduct topic modeling, and forecast stock market prices. Toward the end of the book, you will learn the common applications of GANs and how to build a face generation model using them. Finally, you’ll get to grips with using reinforcement learning and deep reinforcement learning to solve various real-world problems. By the end of this deep learning book, you will be able to build and deploy your own deep learning applications using appropriate frameworks and algorithms.

Who is this book for?

This book is for data scientists, machine learning engineers, and deep learning developers who are familiar with machine learning and are looking to enhance their knowledge of deep learning using practical examples. Anyone interested in increasing the efficiency of their machine learning applications and exploring various options in R will also find this book useful. Basic knowledge of machine learning techniques and working knowledge of the R programming language is expected.

What you will learn

  • Design a feedforward neural network to see how the activation function computes an output
  • Create an image recognition model using convolutional neural networks (CNNs)
  • Prepare data, decide hidden layers and neurons and train your model with the backpropagation algorithm
  • Apply text cleaning techniques to remove uninformative text using NLP
  • Build, train, and evaluate a GAN model for face generation
  • Understand the concept and implementation of reinforcement learning in R
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Publication date : Apr 24, 2020
Length: 330 pages
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Language : English
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Concepts :
Deep Learning
Tools :
MXNet

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ISBN-13 : 9781788996839
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Concepts :
Deep Learning
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MXNet

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Table of Contents

15 Chapters
Section 1: Deep Learning Basics Chevron down icon Chevron up icon
Section 1: Deep Learning Basics
Machine Learning Basics Chevron down icon Chevron up icon
Machine Learning Basics
An overview of machine learning
Preparing data for modeling
Training a model on prepared data
Evaluating model results
Improving model results
Reviewing different algorithms
Summary
Setting Up R for Deep Learning Chevron down icon Chevron up icon
Setting Up R for Deep Learning
Technical requirements
Installing the packages
Preparing a sample dataset
Exploring Keras
Exploring MXNet
Exploring H2O
Exploring ReinforcementLearning and RBM
Comparing the deep learning libraries
Summary
Artificial Neural Networks Chevron down icon Chevron up icon
Artificial Neural Networks
Technical requirements
Contrasting deep learning with machine learning
Comparing neural networks and the human brain
Utilizing bias and activation functions within hidden layers
Surveying activation functions
Creating a feedforward network
Augmenting our neural network with backpropagation
Summary
Section 2: Deep Learning Applications Chevron down icon Chevron up icon
Section 2: Deep Learning Applications
CNNs for Image Recognition Chevron down icon Chevron up icon
CNNs for Image Recognition
Technical requirements
Image recognition with shallow nets
Image recognition with convolutional neural networks
Enhancing the model with additional layers
Choosing the most appropriate activation function
Selecting optimal epochs using dropout and early stopping
Summary
Multilayer Perceptron for Signal Detection Chevron down icon Chevron up icon
Multilayer Perceptron for Signal Detection
Technical requirements
Understanding multilayer perceptrons
Preparing and preprocessing data
Deciding on the hidden layers and neurons
Training and evaluating the model
Summary
Neural Collaborative Filtering Using Embeddings Chevron down icon Chevron up icon
Neural Collaborative Filtering Using Embeddings
Technical requirements
Introducing recommender systems
Collaborative filtering with neural networks
Exploring embeddings
Preparing, preprocessing, and exploring data
Performing exploratory data analysis
Creating user and item embeddings
Building and training a neural recommender system
Evaluating results and tuning hyperparameters
Summary
Deep Learning for Natural Language Processing Chevron down icon Chevron up icon
Deep Learning for Natural Language Processing
Formatting data using tokenization
Cleaning text to remove noise
Applying word embeddings to increase usable data
Clustering data into topic groups
Summarizing documents using model results
Creating an RBM
Defining the Gibbs sampling rate
Speeding up sampling with contrastive divergence
Computing free energy for model evaluation
Stacking RBMs to create a deep belief network
Summary
Long Short-Term Memory Networks for Stock Forecasting Chevron down icon Chevron up icon
Long Short-Term Memory Networks for Stock Forecasting
Technical requirements
Understanding common methods for stock market prediction
Preparing and preprocessing data
Configuring a data generator
Training and evaluating the model
Tuning hyperparameters to improve performance
Summary
Generative Adversarial Networks for Faces Chevron down icon Chevron up icon
Generative Adversarial Networks for Faces
Technical requirements
An overview of GANs
Defining the generator model
Defining the discriminator model
Preparing and preprocessing a dataset
Training and evaluating the model
Summary
Section 3: Reinforcement Learning Chevron down icon Chevron up icon
Section 3: Reinforcement Learning
Reinforcement Learning for Gaming Chevron down icon Chevron up icon
Reinforcement Learning for Gaming
Technical requirements
Understanding the concept of reinforcement learning
Preparing and processing data
Configuring the reinforcement agent
Tuning hyperparameters
Summary
Deep Q-Learning for Maze Solving Chevron down icon Chevron up icon
Deep Q-Learning for Maze Solving
Technical requirements
Creating an environment for reinforcement learning
Defining an agent to perform actions
Building a deep Q-learning model
Running the experiment
Improving performance with policy functions
Summary
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Pawlus
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Michael Pawlus is a data scientist at The Ohio State University where he is currently part of the team building of the data science infrastructure for the Advancement department while also leading the implementation of innovative projects there. Prior to this, Michael was a data scientist at the University of Southern California. In addition to this work, Michael has chaired data science education conferences, published articles on the role of data science within fundraising and currently serves on committees where he is focused on providing a wider variety of educational offerings as well as increasing the diversity of content creators in this space. Michael holds degrees from Grand Valley State University and the University of Sheffield.
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Profile icon Rodger Devine
Rodger Devine
Rodger Devine is the Associate Dean of External Affairs for Strategy and Innovation at the USC Dornsife College of Letters, Arts, and Sciences. Rodger's portfolio includes advancement operations, BI, leadership annual giving, program innovation, prospect development, and strategic information management. Prior to USC, Rodger served as the Director of Information, Analytics, and Annual Giving at the Michigan Ross School of Business. Rodger brings nearly 20 years of experience in software engineering, IT operations, BI, project management, organizational development, and leadership. Rodger completed his Masters in data science at the University of Michigan and is a doctoral student in the OCL program at the USC Rossier School of Education.
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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 [email protected] 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 [email protected] 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 [email protected] 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 [email protected] 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