Rick MALEMBETI

DVA is not associated with this job posting French Advanced AI Data Trainer https://lnkd.in/e76PvvbH The Role As an Advanced AI Data Trainer you will be working closely with a team of other trainers, within protocols developed by the world’s leading AI researchers— training the AI to read, write, summarize knowledge, and interpret meaning. Think of it like being a language arts teacher or a personal tutor for some of the world’s most influential technology. The goal of this AI is to be a safe, accurate, widely knowledgeable, and beneficial conversation partner to the world for a wide variety of purposes. Your job is to train, evaluate, and test the AI’s conversation skills, continuously equipping it to fulfill that purpose. Day in and day out, you will: Train the AI You’ll spend the bulk of your time generating examples of ideal conversations, acting as both the User and the AI for the AI to learn from, collecting sources helping it read large swaths of humanity’s documented knowledge and distinguish between what is presented as fact vs. context vs. patterns of behavior. Evaluate the AI Our partner’s mission is to develop AI models that are safe, accurate, and beneficial to humanity. You will continuously evaluate the AI according to those criteria and our training methods. For example, you will be discerning the accuracy of the facts that the AI is outputting, but also the accuracy with which they interpret them. #freelance #freelancer #graphicdesign #design #art #logo #business #branding #entrepreneur #graphicdesigner #illustration #photography #designer #marketing #artist #creative #illustrator #work #dise #cfbr #smallbusiness #digitalart #instagram #socialmedia #startup #photographer #workfromhome #photoshop #freelancelife #freelancing

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⭐️ New video release 📺: Performant, scientific computation in 🐍Python and 🦀 Rust Watch Stefan Ulbrich as he guides you through building performant scientific computation packages in Python and Rust, offering insights into efficient computation and optimization for machine learning and data science applications. 📺 Watch the video on YouTube: https://lnkd.in/eq-BhHdH Stefan Ulbrich, Senior Data Scientist, offers a tutorial on enhancing scientific computation in Python and Rust. The session explores packaging numerical calculus and algorithms using modern tools. Highlighting the efficiency of vectorization in Python for computational tasks within Machine Learning, Ulbrich presents the integration of Rust to optimize package performance. Rust, recognized for its performance and memory safety, complements Python, particularly for instances prioritizing speed and modern language features. The talk demonstrates creating a scientific Python package, gradually transitioning components to Rust for enhanced performance. Through an example of implementing the "Expectation maximization for Gaussian Mixture Models" algorithm, attendees learn how to leverage modern tools to build, optimize, and extend packages efficiently. Ulbrich emphasizes that while Rust knowledge is not mandatory, the session provides a high-level understanding of its benefits for Python extension and performance improvement. For a hands-on experience, participants are encouraged to clone Ulbrich's repository for swift installation and to optimize session time effectively.

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🤖 Deep Dive Workshop D: Collaboration Between Agents – Techniques for Multi-Agent LLMs and Their Operational Impact Join Donatien Chedom Fotso, Ph.D., AI & ML Team Lead at Deutsche Bank, at Generative AI EUROPE 2024 for an in-depth exploration of how multi-agent LLM systems can revolutionize business operations. Key highlights: *Advanced techniques for improving AI agent collaboration and interaction *Real-world case studies showcasing the operational impact of multi-agent systems *Interactive group discussions and scenario-based activities to explore potential applications *Ideal for AI researchers, developers, and tech strategists looking to push the boundaries of AI capabilities. 🚀 👉 Register now and enhance your AI solutions with multi-agent systems: https://lnkd.in/e783QTaV #GenAIEUROPE #AIConference

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In this talk Sébastien Stormacq shows us how we can easily deploy an existing server-side Swift app to AWS Lambda with no code changes! Then you'll see how to deploy a native function to Lambda and finally how to use the SwiftPM plugin

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#Dart tip: Embrace null safety with the nullish coalescing operator (??)! 💪 final value = nullableValue ?? defaultValue; Join us at https://lnkd.in/eGwCABJe

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⭐️ New video release 📺: Refactoring Large Programs Watch Dr. Kristian Rother guide you through refactoring a large, messy, and undocumented Python program, offering valuable insights and strategies for your next big refactoring challenge. 📺 Watch the video on YouTube: https://lnkd.in/emjn58cb Kristian, a freelance Python trainer, presented a tutorial on refactoring large programs at a recent event. He addressed the challenge of cleaning up complex software with 10,000-100,000 lines of code, particularly in the case of legacy code. The tutorial focused on practical procedures such as code review, writing minimal tests, adding type annotations, and isolating cleanable parts from problematic sections. Kristian emphasized the systematic approach to large-scale refactoring, highlighting that while it is difficult and costly, it can be tackled methodically. The session, aimed at those with fluency in basic Python, included interactive elements and practical exercises for attendees to enhance their refactoring skills. Participants were provided with the messy code and refactoring guidelines through GitHub for hands-on learning.

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In this first article of a new series on RAG, Josselin Thibault overviews the focus areas, including Technical Evolution and Core Components, Implementation Challenges, Advanced Topics and Future Directions. Directions.https://wix.to/X6dg5GC #newblogpost

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Nathaniel Haines, Manager of Data Science Research of Ledger, will be presenting at PyData NYC 2024, where he’ll be introducing BayesBlend - a new, open-source Python package developed by Ledger.  It allows for users to easily blend together predictions from multiple different models in a data-driven way. The talk will feature practical examples from insurance loss modeling. #datascience #python #opensource #casualtyils #insurancesecuritization #casualtyanalytics

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Your Contribution for the start of the week 📆: botpress 👇 Co-founded by Sylvain Perron and Justin Watson, Botpress allows developers to build chatbots without a team of data scientists and machine learning experts. Here’s why you should contribute to this repo: 1️⃣ By contributing to botprocess, you can dive into complex bot development using the Botpress SDK/CLI and your contributions have the chance of positively impacting the experience of hundreds of thousands of developers. Since maintainers take extra care in getting back to you (with a ~100% reply rate and an average of 1 day to answer your queries), your contributions will get the attention they deserve. 2️⃣ Nearly 50% of contributors submit a second PR. This means the repo has a supportive environment for building upon past contributions. If you enjoy working on this repo, chances are you can continue to work on it! ⚡️ 3️⃣ Botpress is an accessible repo. At Quira, we rank Developers by their DevRank (more on this here: https://lnkd.in/eNtrs97F). We found that the median developer working on the repo belongs to the ~85th percentile of GitHub developers. In comparison, many of the open source companies we reported on have had a median contributor level of ~95th percentile. This means that you have a real opportunity to contribute. ⚡️ You can check more of replicate/cog's open-source stats on Quira (https://lnkd.in/efemmXwH) or access the GitHub page https://lnkd.in/eD8SdZBH 📖

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New update ! Episode 8 from this summer: Reusing an existing pre-trained model  How to leverage the power of "transfer learning", a widely adopted technique in deep learning ?  https://lnkd.in/eBHX7UvM

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Nicolas Fränkel 🇺🇦🇬🇪: "My demo of OpenTelemetry Tracing features two Spring Boot components. One uses the Java agent, and I noticed a different behaviour when I recently upgraded it from v1.x to v2.x. In the other one, I’m using Micrometer Tracing because I compile to GraalVM native, and it can’t process Java agents. In this post, I want to compare three methods: Java agent v1, Java agent v2, and Micrometer Tracing."

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https://lnkd.in/efEZ9SHq New to LLMs and #generativeAI ? check out our ebook on "An Introduction to Large Language Models" if you want to better understand how topics such as retrieval augmented generation (RAG) can work in your enterprise.

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In this article, Baptiste L. shows how to leverage the power of reinforcement learning for feature selection when faced with very large datasets

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