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Machine Learning
The landscape of machine learning in the Elixir programming language is diverse and rapidly evolving, with an emphasis on functional programming and powerful concurrency primitives. The Nerves Project, although primarily focused on embedded systems, has seen updates that might contribute to machine learning applications in edge devices. Within this landscape, the FLAME library and Nx are being discussed as formidable tools for constructing elastic workloads, challenging the norms of serverless computing.
Leveraging the strengths of Elixir's functional environment, libraries such as Axon have been developed, offering a fresh perspective to neural network training by providing an Elixir-native API without the need for bridging to Python-based frameworks. This approach not only simplifies machine learning workflows but also enhances scalability. Elixir is also proving itself to be nimble in startup times for machine learning applications, with strategies like Dockerfile caching dependencies, driving faster deployments.
When it comes to integrating AI into Elixir projects, open-source initiatives like Instructor pave the way by addressing common integration challenges such as parsing unstructured data. By utilizing Ecto schemas, Instructor aims to streamline AI integration into standard software systems. Additionally, a variety of Elixir libraries are making machine learning more approachable, with each offering distinct functionalities likened to their Python counterparts. These libraries, including Elixir-Nx, Axon, Bumblebee, and Scholar, enrich the ecosystem with tools for data exploration, model training, and application in real-world apps like semantic search for HexDocs.
Moreover, enhancements in Phoenix reveal an intersection with AI through features such as image recognition and conversational agents, highlighting the flexibility of Elixir in various domains. The language's ecosystem is seen to mature with developers reflecting on its growth particularly in machine learning, underlining projects like Nx that began with unlikely starts but now face a promising future. The practical application of machine learning in Elixir spans several domains, from optimizing language models to developing audio-speech recognition with pre-trained models.
Elixir's vibrant community is contributing to a wealth of educational materials, podcasts, and conferences, where topics range from prototyping AI agents and full-text search engines to using fuzzy logic and machine learning for real-world problems like spam detection. Thought leaders and developers alike are demonstrating the applicability and efficiency of Elixir in machine learning through tutorials, real-world applications, and explorations of model deployment in production environments. The broad and active involvement in areas such as conversational AI, recommendation engines, and prototyping evidences a keen interest in leveraging Elixir's strengths in the AI field. The growing library ecosystem and improvements in areas like quantization and MLIR support promise an exciting future for machine learning applications within the Elixir community.
Using Whisper for Speech Recognition in Elixir Applications
In this blog post, Sean Moriarity introduces Whisper, an audio-speech recognition model developed by OpenAI. He explains the challenges and uses of audio-speech recognition and provides instructions on how to use Whisper in Elixir applications with the help of the Bumblebee library.
Using Scholar for Traditional Machine Learning in Elixir
Sean Moriarity discusses traditional machine learning in Elixir with Scholar, a set of machine learning tools built on top of Nx. He explains how Scholar offers implementations of non-deep-learning models like linear regression and logistic regression, and demonstrates how to fit and visualize a linear regression model using Scholar in Elixir.
Elixir and Open-Source Alternatives to Proprietary LLMs like ChatGPT
In this blog post by Sean Moriarity, he discusses the rise of large-language models (LLMs) like OpenAI's ChatGPT and GPT-4, and explores open-source alternatives to these models. He highlights the benefits of using open-source models, such as data privacy, lower latency, task-specific performance, and cost considerations, and introduces some popular open-source options like Flan-T5, Llama, and OpenAssistant that can be used with Elixir.
Building a Video Object Detection Prototype with Elixir
Philip Brown has built a prototype of object detection in a video stream using Elixir, Bumblebee, and Phoenix LiveView. He provides a step-by-step guide on setting up the project, implementing object detection from a video, and building the LiveView application for displaying the video and predictions in the browser.
Tutorial on Recognizing Handwritten Digits with Elixir ML
In this tutorial by Philip Brown, you will learn how to build an end-to-end machine learning project using Elixir. The tutorial covers everything from setting up the project using Phoenix, obtaining training data, preprocessing the data, building and training the model, and finally, creating a LiveView to accept user input and display predictions.
Optimizing Application Boot Times with Dockerfile Alterations
Jason Stiebs discusses how to optimize boot up time for Machine Learning and Single File Elixir Scripts. By caching dependencies in the Dockerfile during the build step, the boot time can be significantly reduced, resulting in faster application deployment.
Exploring Nx and Tensors Beyond Machine Learning in Elixir
This post by Jason Stiebs explores the use of NX with Elixir for efficient math programming. It explains how tensors can be used to perform various mathematical operations and highlights the potential of NX for tasks like machine learning and image manipulation.
From Python to Elixir Machine Learning
Andres C Alejos discusses the growth of Elixir's machine learning ecosystem and why now is a good time to start porting machine learning code into Elixir. He provides practical tips and examples for developers looking to move from Python to Elixir for machine learning projects.
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.
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