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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.
Future Development Plans for Nx, Axon, and Bumblebee in Elixir
Sean Moriarity discusses the recent advancements and future plans for Nx, Axon, and Bumblebee, highlighting significant concepts such as quantization, LoRA, and model sharding.
Applying Machine Learning for Stock Trading with Elixir
Sam McDavid discusses approaches to implementing machine learning for stock trading using Elixir, emphasizing reinforcement learning and recurrent neural networks.
Exploring Elixir for Machine Learning with Savannah Manning and Bruce Tate
Bruce Tate and Savannah Manning present an engaging session on leveraging Elixir for machine learning, drawing parallels with climbing expeditions.
Utilizing Elixir for Machine Learning in Trading
Sam McDavid presents on applying machine learning techniques to trading using the Elixir programming language at GigCityElixir25.
Introduction to Machine Learning with Elixir, Nx, and Axon
An introduction to machine learning using Elixir and the libraries Nx and Axon, focusing on predicting fuel efficiency.
Podcast episode discussing ElixirConf 2024
Elixir Wizards and Thinking Elixir combine forces to discuss ElixirConf 2024. Hosts Owen, Sundi, David, and Mark recap past conferences and preview upcoming events.
Exploring the Explorer Library and Its Applications in Data Science
Christopher Grainger discusses building powerful tools at Amplified AI and introduces the Explorer library for managing large datasets.
Discussion on Elixir's role in AI and machine learning
Chris, Lars and Alex discuss Chris's journey into Elixir and how he uses machine learning and AI to build a product and a business.
The Journey and Technological Evolution of Elixir
José Valim discusses the history and evolution of Elixir, the language he developed starting in 2012, with insights into its design choices, challenges, and future.
Introduction to Elixir: Key Reasons to Choose This Dynamic Language
This article presents an introduction to Elixir, explaining its creation, core strengths, use cases, and features.
Exploring Machine Learning with Elixir
Zack Siri discusses his journey of learning machine learning using the Elixir programming language.
Enhancements in Elixir's Axon for Machine Learning
Sean Moriarity outlines recent advancements in training machine learning models using Axon in Elixir.
Implementing Machine Learning at Scale in Elixir using Bumblebee and Broadway
Raj Rajhans discusses how Elixir libraries Bumblebee and Broadway can be used to implement machine learning tasks in production, specifically for natural language media search.
Integration of Elixir with Machine Learning Technologies in 2024
Overview of the advancements in integrating Elixir with machine learning technologies in 2024, including updates on MLIR, Apache Arrow, and structured LLMs.
Introduction to Nx and its Implication for Elixir in Machine Learning
José Valim introduces Nx (Numerical Elixir) v0.1, its relevance in numerical computing and machine learning, and expected future developments.
Exploring Rustler with Dave Lucia
Dave Lucia discusses Rustler and Elixir Internals.
Exploring Numerical Elixir with Paulo Valente
Brooklin and the DockYard crew speak with Paulo Valente about machine learning using Numerical Elixir.
Switching to Elixir with Amplified's Chris Grainger
Chris Grainger, CTO of Amplified, discusses his decision to adopt Elixir and the benefits observed from such a choice.
Interview with Jenn Gamble about Data Science and Machine Learning in Elixir
Jenn Gamble discusses her expertise in data science and machine learning, particularly focusing on her experiences at Very, an IoT engineering firm. She elaborates on the differences between Elixir models and data science models, the importance of intuitive algorithms, and the relevance of foundational knowledge in machine learning.
Exploring Machine Learning in Elixir with Sean Moriarity
Sean Moriarity discusses the current state and future direction of Machine Learning in Elixir, sharing insights from his book and his work on Nx.
Experiences and Insights Working with Elixir
_MMCXII asks about Elixir's capabilities and suitability for various tasks, including reasons to choose or avoid it and personal experiences from users.
Recap and Highlights from ElixirConf 2022
Hosts recap ElixirConf US 2022, discussing major announcements, highlights, and other significant tech developments in the Elixir community.
Exploring Elixir, LiveView, Nerves, and Machine Learning
Hosts discuss LiveView, Nerves, and Machine Learning within the BEAM ecosystem.
Exploring the Bumblebee Release in the Elixir Ecosystem
José Valim, Paulo Valente, and Jonatan Kłosko discuss Bumblebee, a tool for using pre-trained neural network models in Elixir.
Discussing Machine Learning in Elixir with Philip Brown
Philip Brown shares insights on using Elixir, Axon, and Nx for machine learning at his company, Prise.
Discussion on GenServers with Kate Rezentes
Kate Rezentes discusses her experience with GenServers at Simplebet.
Highlights and Updates from the Elixir Community
Hosted by Mark Ericksen, David Bernheisel, and Cade Ward, this episode of Thinking Elixir discusses various stories and updates within the Elixir community, including machine learning implementations, LiveView bug fixes, Docker updates, and a new release of Credo.
Elixir's Machine Learning Capabilities for Production Environments
Christopher Grainger provides insights into the production-readiness of machine learning in the Elixir ecosystem, emphasizing deep integration with BEAM and OTP.
Livebook Exploration of Elixir for AI and Web Applications
José Valim, the creator of the Elixir programming language, delivered a presentation on Elixir and the Erlang VM, showcasing their capabilities through Livebook. He demonstrated the power of Elixir for managing concurrent processes, functional programming benefits, and building scalable, fault-tolerant systems. Valim also included a live coding session where a simple web application that predicts text sentiment using AI was developed within Livebook.
Guide to Deploying Nx in Production for Elixir Applications
Christopher Grainger presents a comprehensive guide for leveraging the Nx ecosystem, including Livebook, Nx, Bumblebee, Axon, Explorer, and Scholar, to build B2B SaaS products with Elixir. This roadmap aims to introduce the Nx ecosystem and demonstrate its production readiness.
Keynote Overview on Implementing Nx in Production
Code Sync: A discussion from ElixirConf EU 2024 on the best practices for integrating Nx into production systems.
Rapid Deployment of AI Apps Using Elixir's Livebook
Andrés Alejos discusses the rapid validation and deployment of AI applications using Elixir's Livebook. By leveraging the Elixir ecosystem, it's possible to quickly create and test AI-driven products without long development cycles.
Using Elixir and Whisper AI for Podcast Transcription in Livebook
The video showcases how to use the Elixir programming language and the Whisper AI model to transcribe podcasts quickly and efficiently using Livebook.
Discussing Elixir's Role in Developing the Erlang Ecosystem
The discussion features insights from Francesco Cesarini, founder of Erlang Solutions, and Andrea Leopardi, a member of the Elixir Core Team. They delve into the evolution of Erlang from a singular language to a diverse ecosystem, detailing the emergence of various languages on the BEAM and the influence of Elixir on Erlang's ongoing development.
Unpacking the Influence of Elixir on Erlang's Evolution
Francesco Cesarini and Andrea Leopardi discuss the growth of Erlang into a diverse ecosystem with the emergence of languages like Elixir on the BEAM virtual machine. They highlight Elixir's impact on Erlang, noting challenges and opportunities in extending the Erlang VM.
Exploring Machine Learning in Elixir through Clustering and Structured Prompting with Bumblebee
Sean Moriarity discusses the potential of Machine Learning with Elixir, emphasizing the new applications enabled by structured prompting and clustering.
Integrating ONNX Models into Elixir with Ortex
Sean Moriarity explores how to integrate pre-trained ONNX models with Elixir applications using Ortex, including voice activity detection for conversational AI.
Designing a Deep Learning Framework with Elixir's Axon
Sean Moriarity presents on creating and training neural networks in Elixir using Axon, a library he developed. Axon differentiates itself from Python-based deep learning frameworks by leveraging Elixir's functional programming environment, offering a simple, straightforward API with native Elixir code, and scalability.
Discussing AI Integration with Elixir Projects
pkrawat1 shares his experience with integrating AI into Elixir projects and opens a discussion for Elixir enthusiasts to exchange stories and ideas about using AI with Elixir. Specifically, he mentions his successful use of the LangChain library by Mark Ericksen, integrating it into a personal trading app and creating a helper Agent.
Reflecting on a Year of Growth in Elixir Through Livebook and Open Source Contributions
Andrés Alejos reflects on his experiences over his first year of working with Elixir, focusing on machine learning, Livebook applications, and contributions to open source.
Exploring Advanced Machine Learning with Elixir’s FLAME and Nx Libraries
Sean Moriarity discusses how the FLAME library and Nx are shaping the future of elastic workloads in machine learning, positioning themselves as an alternative to traditional serverless solutions.
Overview of Nerves Project Developments
Frank Hunleth presents updates on the Nerves Project, which is significantly used in embedded systems within the Elixir ecosystem. The update covers new versions of hardware, libraries, and protocols, emphasizing the project's expansion and improvements.
Building Conversational Agents with Elixir
Sean Moriarity discusses the development of conversational agents using Elixir technologies.
Using Nomic Embed with Elixir Bumblebee for Text Embeddings
Nate Todd explores the ease of using Nomic Embed, an open-source text embedding model, with the Elixir programming language leveraging the Bumblebee library.
Leveraging BEAM for a Resilient, Load-Balanced Elixir App
definitive_solutions is exploring the process of building a distributed Elixir application to leverage the BEAM VM's capabilities for resilience and load-balancing. The author is considering the [fly.io](https://fly.io) hobby plan along with utilizing a personal server for more compute-intensive tasks.
Understanding Instructor: Elixir's Gateway to AI Integration
Thomas Millar introduces Instructor, an open-source project to bridge Elixir with AI, focusing on machine learning applications and their integration into Elixir projects. The project addresses challenges developers face when integrating AI, such as backward incompatibility and the difficulty of parsing unstructured data. Instructor leverages Ecto schemas for structuring data expected from AI models, streamlining the process and making AI more accessible to standard software systems.
Ranking of ElixirConf EU 2023 Talks Based on Popularity
TechTalksWeekly has compiled and ranked ElixirConf EU 2023 talks based on the number of views on YouTube.
Exploring Elixir as a New Programming Language in 2024
pudge_dodging recounts their experience with starting to learn a new programming language every year, this time considering Elixir after not enjoying Rust the previous year. They ask the community for advice on whether to choose Elixir or Erlang, resources for learning Elixir, project ideas, and the relevance of Elixir in 2024, especially concerning machine learning.
Understanding Elixir's Full Stack Capabilities
Lars Wikman presents on the capabilities of the Elixir programming language and the Phoenix web framework for building web applications. He discusses the advantages of Elixir, such as developer productivity, performance, reliability, and observability.
Exploring Livebook and Computational Notebooks in Elixir
José Valim, creator of Elixir, shares insights on Livebook, a computational notebook for Elixir, emphasizing the integration of code, documentation, and rich visualizations within a functional programming environment. In his Lambda Days 2023 presentation, Valim highlights the importance of immutability in Elixir and how this trait, along with the process model, enables reproducible workflows in Livebook.
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