Deep Learning in R Programming

Deep Learning is a subset of Artificial Intelligence (AI) that mimics the human brain's structure and function to process data and create patterns for decision-making. It is part of Machine Learning (ML) and involves networks capable of unsupervised learning from unstructured or unlabeled data. These systems can learn automatically from various data sources. By using hierarchical artificial neural networks, deep learning allows for the quick processing of large amounts of unstructured data, something that would usually take humans decades to analyze.

Why Use R for Deep Learning

R Programming Language is popular in the data science and machine learning communities due to its rich ecosystem of libraries and packages for statistical analysis and machine learning. Therefore it has the following advantages when using it for deep learning:

1. Wide Range of Packages: R offers an extensive collection of libraries that make implementing deep learning algorithms simpler.
2. Ease of Use: With its high-level syntax, R can be used for quick experimentation and prototyping since it allows to build deep learning models without having to write extensive code.
3. Integration with Other Tools: R can integrate with other machine learning frameworks such as TensorFlow and Keras, enabling users to use more advanced features.
4. Visualization: R is widely used for its visualization capabilities, which allow users to visualize model training, performance and results in a user-friendly manner.
5. Statistical Analysis: Since R is used in data manipulation and statistical modeling, it is an excellent choice for tasks requiring deep data exploration and analysis.

Packages for Deep Learning in R

R Programming Language has many deep learning packages in CRAN. Some of these packages are as follows :

1. Introduction to Deep Learning

In this section, we will explore the fundamentals of deep learning, including the different types of neural networks, their architecture and the role of activation functions in shaping their learning process.

• Types of Neural Networks
• Architecture and Learning Process in Neural Networks
• Artificial Neural Networks and Its Applications
• Activation Functions in Neural Networks Using R

2. Core Neural Network Concepts in R

In this section, we will dive into the core concepts of neural networks, focusing on feedforward networks, the importance of layers, neurons and how epochs influence learning.

• Pooling Layers
• Feedforward Neural Networks (FNNs)
• Single-Layered Neural Networks
• Multi-Layered Neural Networks

3. Neural Networks and Variants in R

In this section, we will cover the advanced concepts of Recurrent Neural Networks (RNNs) and their variants, such as GRU and LSTM, that are specifically designed for sequential data.

• Recurrent Neural Networks
• What is GRU
• Long Short-Term Memory (LSTM)

4. Image Processing and Working with Images in R

In this section, we will explore how to work with images in R, including scaling, arithmetic operations and leveraging the magick package for image manipulation.

• The magick Package
• Scaling Image
• Image Arithmetic

5. Advanced Neural Network Architectures in R

In this section, we will look at more sophisticated neural network architectures, like Convolutional Neural Networks (CNNs) for image recognition and Generative Adversarial Networks (GANs) for creative applications.

• Convolutional Neural Networks (CNNs)
• Generative Adversarial Networks (GANs)

6. Training and Optimization Techniques in R

In this section, we will discuss the key techniques for training neural networks, such as stochastic gradient descent, batch size and optimizing for accuracy instead of loss.

• Epoch in Neural Network
• Gradient Descent Algorithm
• Stochastic Gradient Descent
• Bias Neurons
• Train and Test Neural Networks
• Optimizing for Accuracy Instead of Loss in Keras Model
• Batch Size
• Hyperparameter Tuning

7. Specialized Loss Functions and Techniques in R

In this section, we will examine advanced topics like custom loss functions in Keras and gradient boosting, which are important for improving model performance and accuracy.

• Custom Loss Function with Keras
• Boosting
• Gradient Boosting

Implementation of Neural Network in R

We will be using the deepnet package for implementing deep learning in R programming language.

1. Installing and Loading the Packages 

We will install the deepnet and mlbench packages using the install.packages() function and load them using the library() function.

install.packages("mlbench")
install.packages("deepnet")
install.packages("data.table")

library(data.table)
library(mlbench)
library(deepnet)

2. Loading the Dataset  

We will work with the Breast Cancer Dataset under the mlbench package. We will choose those rows where the data is not incomplete or missing.

df <- as.data.table(BreastCancer)

df <- df[complete.cases(df)]
head(df)

Output:

3. Data Preprocessing

We will create a set of features for independent variables and create the dependent variable.

df[, Class := ifelse(Class == "malignant", 1, 0)]

y <- df$Class
x <- as.matrix(df[, -'Class', with = FALSE])
x <- matrix(as.numeric(x), ncol = 9)

4. Training the Neural Network

We will apply nn.train() function under the deepnet package to train the neural network.

set.seed(23)

nn <- nn.train(x, y, hidden = c(5))
y_pred = nn.predict(nn, x)

cat("Predicted Output:\n",head(y_pred),sep = "\n")

Output:

5. Creating a Confusion Matrix

To create a confusion matrix, use the table() function. Also we can check the accuracy of the confusion matrix by dividing the sum of the diagonal elements with the total count or sum of all the numbers.

yhat <- ifelse(y_pred > mean(y_pred), 1, 0)

cm <- table(y, yhat)
print(cm)

accuracy <- sum(diag(cm)) / sum(cm)
cat("\nThe accuracy of our model is:",accuracy*100,"%")

Output:

We can further increase the accuracy of our model by fine-tuning as well as performing feature selection.

Machine Learning in R

Machine learning focuses on developing algorithms that helps computers to learn from data and make predictions or decisions without explicit programming. R provides extensive support for both supervised and unsupervised learning algorithms.

• Machine Learning with R Programming

To get a detailed overview of R programming, you can refer to: R Programming Tutorial