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We can use Ruby to do anything we as a community want. Today we’ll explore the work of a hidden gem of a contributor in our community, Andrew Kane, and their Ruby gems for Machine Learning. We will see how contemporary computer vision neural networks can run with Ruby. Ruby is all about developer happiness. Computer Vision is something that brings me great joy as it delivers satisfying visual feedback and connects our code with the real world through images and videos in a way that wasn’t accessible until the last decade or so.
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### Summary of "Discover Machine Learning in Ruby" In the talk "Discover Machine Learning in Ruby" presented by Justin Bowen at RubyConf 2022, the speaker explores the intersection of Ruby programming and machine learning, a field not commonly associated with Ruby. The presentation emphasizes the capabilities of Ruby to incorporate machine learning and showcases the contributions of Andrew Kane, a notable figure in the Ruby community. #### Key Points Discussed: - **Introduction to Speaker's Background**: Justin Bowen discusses his extensive experience in programming with Ruby and Python, especially focusing on computer vision applications since 2008. - **Overview of Computer Vision**: He describes his enthusiasm for computer vision and the exciting opportunities it presents for interactive visual feedback in programming. - **Importance of Andrew Kane**: Bowen highlights Andrew Kane as a significant contributor to Ruby’s machine learning community, crediting him with multiple popular Ruby gems such as StrongMigrations, Searchkick, and Chartkick that enhance Ruby’s capabilities. - **Ruby Gems for Machine Learning**: The talk details several notable gems: - **StrongMigrations**: Popular for safely managing migrations with 23 million downloads. - **Searchkick**: Enables effective full-text searches utilizing Elasticsearch and offers offline re-indexing. - **Chartkick**: Simplifies the generation of charts in Ruby. - **Other Noteworthy Gems**: Mention of several others like PG Hero, Lockbox, and ThunderSVM, all contributing to Ruby’s machine learning capabilities. - **Potential of Ruby in Machine Learning**: Bowen argues that, with collective community interest and effort, Ruby can be a viable option for machine learning. - **Encouragement for Contributions**: He encourages developers to explore and contribute to the Ruby ecosystem, emphasizing that participation doesn't require advanced expertise in data science. - **Community and Collaboration**: The importance of a curious and collaborative community in developing advanced machine learning solutions using Ruby. #### Conclusion: Bowen wraps up by reinforcing the message that machine learning is possible in Ruby through community efforts and the utilization of available resources. He encourages attendees to experiment with the Ruby ML stack and consider contributing to this evolving area. In summary, this talk highlights the exciting possibilities of integrating machine learning into Ruby and acknowledges the contributions of key community figures, calling for greater exploration and collaboration in this field.
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