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Livebook
The Elixir programming language, known for its functional nature and concurrency model, has become a powerful tool for an array of applications—ranging from machine learning to educational purposes. People are sharing their experiences of working with Elixir over extended periods, highlighting its ability to leverage machine learning for practical applications, such as semantic search implementation for HexDocs or using GPUs for real-world scenarios on platforms like Fly.io. Elixir's machine learning ecosystem is constantly expanding with libraries like Nx, Axon, and Bumblebee proving to be game-changers, echoing familiar functionalities from Python's toolkit.
When it comes to education, Elixir's Livebook is praised for revolutionizing the learning experience, with its interactive notebooks playing a pivotal role in curriculum development. This technology is not just about scripting; it allows seamless combination with live applications for enhanced interactive learning, as well as native iOS development. Educators and learners alike are leveraging Livebook to create dynamic, rich educational content that reinforces Elixir's standing in the educational sphere.
Community contributions extend beyond education into observability, security, and tooling for the Elixir ecosystem. With features unique to the BEAM runtime, real-time monitoring and debugging using Elixir tools make applications more reliable. Security remains a focus as well, with community leaders sharing best practices and contributing to secure coding courses using Elixir Livebooks. The Elixir community keeps evolving, discussing everything from core language improvements, engagement in open-source contributions, and anticipation of future advancements such as potential new typing systems.
Its utilities are vast, encompassing applications in CLI, data manipulation, and even offering solutions to endearingly mundane problems like organizing Secret Santa exchanges. Elixir's creator, José Valim, continues to lead the charge with keynotes on the future of Elixir, reflecting on the importance of immutability and process models for creating reproducible, stable workflows. Livebook's capabilities are frequently in the spotlight, providing computational notebooks that integrate code, documentation, and visualizations into a solitary ecosystem.
Incorporating Functional Programming into Education
Michał Ślaski, co-founder of koderki.pl and Manager at Google Cloud, discusses his experiences with teaching programming in schools and the challenges associated with effectively engaging students, particularly young girls, in the field of computing.
Insights on Elixir Development and Learning Experience
José Valim presents a keynote speech on updates and the future of Elixir, including development and learning experience enhancements.
Bringing Machine Learning Capabilities into Elixir with Bumblebee
Jonatan Klosko presents Bumblebee, an Elixir tool that simplifies using pre-trained machine learning models.
Elixir's Impact on Machine Learning and Production Workflows
Christopher Grainger explores the integration of machine learning in production within the Elixir ecosystem, highlighting the use of Nx, Livebook, and Scholar.
Securing Software with Elixir: Business and Technical Insights
Michael Lubas discusses the importance of security in Elixir software development, including how the language's design promotes safe concurrency and prevents common vulnerabilities like data races.
Three-Year Growth of Elixir's Nx Machine Learning Library
Sean Moriarity reflects on the growth and success of the Elixir machine learning ecosystem over the past three years. He discusses the unlikely start of the Nx project, the challenges faced, and the future of machine learning in Elixir.
Managing Files in Elixir's Livebook
Peter Ullrich shares a cheatsheet on how to work with files in Livebook. He explains how to upload files, process their contents, and download files with ease.
Introduction to Ash Framework using Elixir
A Gentle Primer to Ash provides an introduction to the Ash Framework in Elixir, explaining its features and how to use it for data extraction and manipulation from CSV files.
Summary of Machine Learning Developments in Elixir for Q3 2023
José Valim explores the latest developments in Elixir and Machine Learning in his blog post, highlighting the improvements in Nx, Explorer, Bumblebee, Scholar, and other projects. The future looks bright as optimization areas, like quantization and MLIR support, gain further attention.
Exploring Elixir's Unique Observability Features
Lars Wikman shares insights on the observability features unique to the BEAM runtime and how Elixir has excellent tools for monitoring and debugging applications.
Integrating Machine Learning Models with Elixir Using Nx
Andrés Alejos presents a talk at ElixirConf 2023 about using EXGBoost + Mockingjay, a Gradient Boosted Decision Tree library, in Elixir for learning structured tabular data and its application in a scalable production environment using Nx's Serving capability and a Phoenix web app.
Streamlining MLOps with Elixir's Capabilities
Sean Moriarity shows how to do MLOps in Elixir, simplifying the deployment of machine learning models without much effort.
Understanding Stochastic Gradient Descent Using Elixir
Eric Iacutone helps build intuition about SGD through an interactive Livebook example in his talk at ElixirConf 2023.
Serving Spam Detection With XGBoost and Elixir
Learn how to detect spam with XGBoost and Elixir and serve the model for production use. Andres C Alejos shares their process and insights in this informative article.
Insights into Elixir's Machine Learning Libraries
Andres C Alejos provides an introduction to machine learning in Elixir and offers a glossary of libraries in the Elixir machine learning ecosystem. He covers libraries such as Elixir-Nx, Axon, Bumblebee, Scholar, Explorer, Scidata, EXGBoost, Ortex, Livebook, and more, highlighting their functionalities and similarities to popular Python libraries.
Exploring OpenAI's API through Elixir for Scheduling Tasks
Jason Pollentier explores the use of OpenAI's Functions API to create an "Agent" that interacts with a calendar service. By emulating a portion of the API, he demonstrates how the model can accurately respond to prompts and perform tasks such as retrieving schedules and scheduling events.
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