Fresh product updates! 🎉 With our new conditional filters in chart blocks, you can now drill down into data points effortlessly without coding. Plus, accessing Deepnote AI is now quicker with simple keyboard shortcuts. Discover all the new features in our changelog. https://lnkd.in/dbwdcAPW
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Unlock the future of coding! 🧠✨ Dive into how AI tools like Cursor and Claude Sonnet can revolutionize your workflow. Efficiency meets innovation—ready to code smarter, not harder? Check out the game-changing insights here: https://lnkd.in/ggaD7N5B
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Our CEO, A. Kirimgeray Kirimli, dives deep into how AI is reshaping software engineering in his latest article on HackerNoon. From AI copilots that code and debug to the evolving roles of engineers in managing AI tools—discover how AI is revolutionizing the way we work, the skills needed, and the challenges ahead. Curious about the future of engineering with AI? Check out the full article to learn more! https://lnkd.in/dr28h-zX
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Many are finding that fine-tuning with a small but high-quality dataset suffices to achieve peak performance in their LLM applications. Sounds simple, but how do you choose and evaluate models and data for YOUR application? At AIQCon, I'll share practical AI engineering workflows and evaluation techniques appropriate for each stage of development to help you move efficiently and reach the best results. Use the code "BeOurGuest" to get 20% off your ticket 🤩 https://lnkd.in/e8pSNSTF
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How do AI software engineering agents work? No better place to start than "at the source:" the state-of-the-art open source AI coding agent: SWE-bench has quietly become the industry standard AI coding agent benchmark. The team behind SWE-bench built SWE-agent (at release, state-of-the-art open source AI agent) and with Ofir Press (a member of the team building SWE-bench and SWE-agent), we dive deep on how it works: Read the deepdive here: https://lnkd.in/e3Ha39Rz
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Curious about LLMs but don't know where to start? 🤖 Let’s get started with latest newsletter issue by Josep Ferrer where he breaks down the essentials for you! 👇🏻 LLMs, like GPT-4, are transforming industries by enabling machines to understand and generate human-like text. But how do you harness this power? 🛠️ First things first: understanding the basics of LLMs and their capabilities. 1️⃣ 𝗖𝗵𝗼𝗼𝘀𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗟𝗟𝗠 𝗳𝗼𝗿 𝘆𝗼𝘂 Not all models are created equal. Assess your needs—are you writing code, generating content, or analyzing data? There’s an LLM tailored for each task. 2️⃣ 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗿𝗶𝗴𝗵𝘁 📊 LLMs thrive on data. Gather, clean, and preprocess your data to ensure the model performs at its best. Remember, garbage in, garbage out! 3️⃣ 𝗧𝗲𝗮𝗰𝗵 𝘆𝗼𝘂𝗿 𝗺𝗼𝗱𝗲𝗹 🧠 Fine-tuning is key. Train your LLM on your specific data to tailor its responses to your needs. This step personalizes the model’s output to suit your unique requirements. 4️⃣ 𝗣𝘂𝘁 𝗶𝘁 𝗶𝗻𝘁𝗼 𝗮𝗰𝘁𝗶𝗼𝗻 🚀 Deploy your model and start seeing the magic. Whether it’s chatbots, content generation, or complex data analysis, watch your LLM transform the way you work. Unlock the full potential of LLMs by following these steps and keep exploring the cutting-edge of AI. Curious for more? Dive deep into the full article for detailed insights: How to Get Started with LLMs 👉🏻 https://lnkd.in/dFZ-ZqKv 🌐 Join our community of AI enthusiasts! Subscribe to our newsletter for more tips, tricks, and tutorials on mastering the latest in AI tech. Let's innovate together! 🚀 👉🏻 Subscribe Now on https://lnkd.in/dE-4xidh
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Computer Vision | Machine Learning | Deep Learning | Image Processing | Flask | ASP.Net Web API | SQL | Python | C#
👉A TensorFlow-based CNN model that accurately classifies handwritten digits from the MNIST dataset! #DeepLearning #ComputerVision #AI" CodexCue Software Solutions .
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Open-source model surpasses state-of-the-art private models in the code generation tasks Beats DeepMind models A new method to generate code . see inside (links to the blog post and the paper) https://lnkd.in/gXi3Cenz
Santiago (@svpino) on X
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Bringing open MLLMs closer to GPT4V🔥 📝 "𝐋𝐢𝐬𝐭 𝐈𝐭𝐞𝐦𝐬 𝐎𝐧𝐞 𝐛𝐲 𝐎𝐧𝐞" is a new learning paradigm. Enables models to understand visual tags, enhances reasoning capabilities. 🚧 Using curated datasets with visual instructions, MLLMs can learn to enumerate🔢, and describe tagged objects in images 🖼️ ✅ A small dataset of 10-30k tagged images greatly improves visual reasoning, reduces hallucinations in MLLMs 🧠 These improvements persist even without visual tags during inference, suggesting new powerful training approach💡 🔍Want to explore this further? Check out the Model + Data on Hub: https://lnkd.in/g8hhu_aB 😀 Code released 🤝 Gradio demos are available locally on the official Repo: https://lnkd.in/g-jiVUFH
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I would like to announce, my first real AI project (work in progress) It's a smart canvas that can evaluate math operations with variables support. It evaluate automatically once the user is idle for three seconds. I have trained a CNN model myself for a better challenge :) GitHub repo: https://lnkd.in/d2iWQ58U a small demo"
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Last week, we introduced our first model Flow-Judge-v0.1 and the flow-judge library. We’re now showcasing how you could use our model in real-world scenarios with a set of practical tutorials. The flow-judge library offers AI developers a powerful evaluation tool that helps scale and optimize LLM system evaluation with precision and flexibility. The first three tutorials guide you through the following: 🚀 Quickstart: Learn how to effortlessly run evaluations with a few lines of code using the flow-judge library. https://lnkd.in/dQ65t3h9 🎨 Custom metrics: Build domain-specific evaluation criteria using custom rubrics. Define metrics that match your unique requirements and integrate them with Flow Judge to score LLM outputs on exactly what matters most to your project. https://lnkd.in/dafmqgsR 📊 Multi-faceted evaluation: Learn how to implement a comprehensive evaluation strategy by combining multiple metrics. https://lnkd.in/dWXEqfk6 Explore the full tutorials on the flow-judge repository: https://lnkd.in/dB2hmGTg
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