Welcome to my blog, where we dive deep into the fascinating world of Machine Learning (ML) and how it can be beautifully integrated with Ruby, along with a touch of Elixir. As a software engineer and PhD candidate specializing in AI and ML, I’m excited to share my insights and experiences.

Ruby’s Role in Machine Learning

Ruby, known for its elegant syntax and readability, might not be the first language that comes to mind when thinking about ML, but it holds unique advantages. It’s user-friendly, which makes prototyping ML models more accessible and less time-consuming. In this post, we’ll explore how to leverage Ruby for ML tasks.

Getting Started with ML in Ruby

To begin, we’ll use the ruby-fann gem, a Ruby binding to the Fast Artificial Neural Network (FANN) library. This gem allows us to create, train, and test neural networks in a Ruby-friendly way.

Installation: First, install the gem with gem install ruby-fann.

Building a Simple Neural Network

Let’s create a basic neural network to perform a simple classification task. We’ll train a model to distinguish between two categories based on input data.

require 'ruby-fann'

# Sample data: [Inputs, Desired Outputs]
train_data = [
  [[0.0, 0.0], [0.0]],
  [[0.0, 1.0], [1.0]],
  [[1.0, 0.0], [1.0]],
  [[1.0, 1.0], [0.0]]

# Creating a neural network: 2 inputs, 1 output, and 3 neurons in the hidden layer
fann = RubyFann::Standard.new(num_inputs: 2, hidden_neurons: [3], num_outputs: 1)

# Training the network
fann.train_on_data(train_data, 1000, 10, 0.1) # 1000 max epochs, 10 errors between reports, 0.1 desired MSE (Mean Squared Error)

# Testing
puts fann.run([0.1, 0.2]).inspect # Testing with a sample input

In this example, we have trained a simple neural network to predict outputs based on inputs. The network’s accuracy and performance can be improved by tuning parameters, adding more data, or increasing the complexity of the network.

Elixir’s Touch

While Ruby is great for the above tasks, Elixir can also contribute, especially when dealing with concurrency and scalability. In a future post, I’ll delve into how Elixir can be used to manage data pipelines and handle large-scale data processing, complementing Ruby’s ML capabilities.


Machine Learning in Ruby offers a great balance between ease of use and effectiveness, especially for those who are starting out or looking for rapid prototyping capabilities. As we continue to explore this intersection, I’ll share more complex examples, best practices, and tips on integrating Ruby with other technologies like Elixir for robust ML applications.

Stay tuned for more!