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RubyConf 2016 - Dōmo arigatō, Mr. Roboto: Machine Learning with Ruby by Eric Weinstein Machine learning is a popular and rapidly growing field these days, but you don't usually hear it mentioned in the same breath as Ruby. Many developers assume that if you want to do machine learning or computer vision, you've got to do it in Python or Java. Not so! In this talk, we'll walk through training a neural network written in Ruby on the MNIST dataset, then develop an application to use that network to classify handwritten numbers.
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The video titled 'Dōmo arigatō, Mr. Roboto: Machine Learning with Ruby' features Eric Weinstein at RubyConf 2016, focusing on implementing machine learning in Ruby, a language not typically associated with this field. Key Points discussed in the talk include: - **Introduction to Machine Learning**: Weinstein clarifies that machine learning is not limited to languages like Python or Java, emphasizing the potential of Ruby for such tasks. He dedicates the talk to his brother, recalling the excitement of the Ruby community and the importance of the content being accessible to all skill levels. - **Understanding Machine Learning Concepts**: The talk introduces machine learning principles, such as supervised learning, neural networks, and the concept of generalization, explaining how machines can identify patterns in data. He discusses the importance of having a basic understanding of high school level mathematics for engagement with machine learning algorithms. - **Supervised Learning with MNIST Dataset**: The MNIST dataset, which consists of handwritten digits, is highlighted as it serves as an excellent example for classification tasks. Weinstein describes preparing data, including defining features and labels, and splitting the dataset into training and test sets. - **Neural Networks Basics**: He explains how neural networks operate similarly to biological neurons, training the model through a method called backpropagation, which iteratively adjusts weights to improve accuracy. He notes the distinction between memorization and generalization in training machines. - **Implementation in Ruby**: The practical application of concepts is demonstrated through using the Ruby gem 'Ruby Fann' to train a neural network to classify numbers in the MNIST dataset. Weinstein illustrates the setup of the neural network, discussing input nodes, hidden layers, and the significance of monitoring overfitting during model training. - **Demo Presentation**: The talk culminates in a live demonstration where Weinstein showcases a developed application that predicts numbers drawn by users, further reinforcing the concepts discussed. - **Takeaways and Call for Community Engagement**: The conclusion emphasizes the opportunity for the Ruby community to grow by actively contributing to machine learning projects, while also urging caution regarding biases in data that can lead to skewed model outputs. In summary, Weinstein fosters a message of empowerment within the Ruby ecosystem, encouraging developers to leverage machine learning and collaborate to enhance the tools available.
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