Alessandro Risaro

Unlocking science through software with the developers of #Nextflow 👩‍🔬 📈 From tackling scalability and portability challenges in computational data pipelines to building an enterprise platform that empowers scientists worldwide. Hear from our CEO Evan Floden and CTO Paolo Di Tommaso on why they created #Nextflow and how #Seqera was built on this foundation to advance science for everyone through software.

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A Python library for options pricing (free on GitHub): Includes closed-form solutions for: • Black Scholes • Merton • Black 76 • Asian 76 • American (And more.) Here's the GitHub link: https://lnkd.in/gKy75WZb ~~~ Looking to start using Python for systematic trading? Here's a free Crash Course with everything you need to get started. Join the 1,000s of people who finally started with Python after reading it: https://lnkd.in/eaayJEfj

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🚨 𝗖𝗼𝗺𝗶𝗻𝗴 𝗦𝗼𝗼𝗻: 𝗧𝗵𝗲 𝟮𝗻𝗱 𝗘𝗱𝗶𝘁𝗶𝗼𝗻 𝗼𝗳 𝙂𝙧𝙖𝙥𝙝 𝙈𝙖𝙘𝙝𝙞𝙣𝙚 𝙇𝙚𝙖𝙧𝙣𝙞𝙣𝙜 by Aldo Marzullo, Enrico Deusebio, and Claudio Stamile 🚨   📊 As machine learning evolves, so do the tools and frameworks we rely on. That’s why we’re excited to announce the upcoming release of this 𝗳𝘂𝗹𝗹𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱 𝗲𝗱𝗶𝘁𝗶𝗼𝗻, featuring 𝗻𝗲𝘄 𝗰𝗵𝗮𝗽𝘁𝗲𝗿𝘀 on 𝗹𝗮𝗿𝗴𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 (𝗟𝗟𝗠𝘀), 𝘁𝗲𝗺𝗽𝗼𝗿𝗮𝗹 𝗴𝗿𝗮𝗽𝗵 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴, and 𝗺𝗼𝗱𝗲𝗿𝗻𝗶𝘇𝗲𝗱 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝘂𝘀𝗶𝗻𝗴 𝗣𝘆𝗧𝗼𝗿𝗰𝗵 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝗶𝗰 𝗮𝗻𝗱 𝗗𝗚𝗟.   🔍 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: • Practical graph ML use cases in NLP, fraud detection, and social networks • End-to-end examples with modern libraries like PyTorch Geometric, StellarGraph & DGL • Techniques to enhance ML applications with LLMs and dynamic graphs • A framework-first approach — helping readers 𝘵𝘩𝘪𝘯𝘬 in graphs, not just code them • Free PDF eBook included with print or Kindle purchases   👥 Ideal for: Data scientists, ML practitioners, and graph engineers looking to take their graph skills to the next level.   📘 Pre-order your copy now and be among the first to explore the future of graph machine learning: 🔗 https://packt.link/lsQVX    #ComingSoon #GraphMachineLearning #LLMs #GraphNeuralNetworks #MachineLearning #DataScience #TemporalGraphs #PyTorchGeometric #DGL #PacktBooks #BookAnnouncement

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📝 Technical tutorial: How to implement GraphRAG in combination with fine-tuned GNN+LLM models to achieve 2x accuracy compared to standard baselines. ➡️ https://lnkd.in/gGPpQZxG This approach is particularly valuable for scenarios involving: 📖 Domain-specific knowledge (product catalogs, supply chains) 🏦 High-value proprietary information (drug discovery, financial models) 🔏 Privacy-sensitive data (fraud detection, patient records)

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Starting today, Claude Sonnet 4 supports 1 million tokens of context on the Anthropic API—5x more than before. This enables developers to process over 75,000 lines of code or dozens of research papers in a single request, ideal for data-intensive uses like long-running agents. Long context support for Sonnet 4 is now in public beta, starting with API users on Tier 4 and custom rate limits. Broader availability on our API will be rolling out over the coming weeks. It’s also available in Amazon Bedrock, and is coming soon to Google Cloud's Vertex AI. Learn more about Sonnet 4 and the 1M context window: https://lnkd.in/e-tfCYBg

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Traditional RAG has limits. #GraphRAG doesn’t. By combining graph-native context with agentic architecture, LLMs can reason better, answer more accurately, and operate more autonomously. In this post, Kumar Subbiah Shunmugathai breaks down NeoConverse — a hands-on experiment in GraphRAG + agentic AI from Neo4j. https://bit.ly/3RumuJQ #GenAI #LLMs #Neo4j #AgenticAI

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New tutorial | Boosting efficiency with batch inference and Ultralytics YOLO! 🚀 This tutorial dives into batch inference fundamentals, its implementation in Python, and the impact of varying batch sizes on inference speed. Key highlights: ✅ Python code walkthrough for implementing batch inference. ✅ Running batch inference in Google Colab. ✅ Measuring inference speed at different batch sizes (1, 16, 32). ✅ Benchmarking efficiency and performance optimization. Watch now ➡️ https://ow.ly/bIkK50VcrXU

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🔍 𝐑𝐀𝐆 𝐯𝐬 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐑𝐀𝐆 𝐯𝐬 𝐆𝐫𝐚𝐩𝐡-𝐑1 AI retrieval is moving fast — and it’s reshaping how we discover knowledge! ✅ RAG (Retrieval-Augmented Generation): Relies on embeddings + vector databases for quick, context-based answers. ✅ Graph-R1: Brings reasoning, planning, and reinforcement learning into play for higher accuracy. ✅ Agentic RAG: Goes beyond — with multi-agent workflows, memory, and adaptive planning for deep, contextual discovery. This evolution shows how AI is shifting from simple retrieval → intelligent, agent-driven knowledge systems. 🚀 What do you think — is Agentic RAG the future of enterprise AI search? Follow AVA® - An Orange Education Label for more on AI, innovation, and the future of knowledge discovery! #AI #RAG #GenAI #KnowledgeDiscovery #Innovation #AVA #OrangeEducation #Publishers #Technology #itandsoftware

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Your NLP model is failing to understand the context of a sentence. Marcello Politi explains how Transformers revolutionized NLP by taking the entire context into consideration to generate a word's vector representation.

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