Robert Berke

The Rails 8 feature set perfectly complements SQLite's power in creating resilient, high-performance production apps, but still the question lingers: Can I really go all-in on #SQLite? Join Stephen Margheim at #RailsWorld as he illustrates how to leverage Rails and SQLite's full potential in your next venture, and when SQLite does and doesn't make sense for your application. https://lnkd.in/eaBstScy

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𝐒𝐕𝐌: 𝐀𝐧 𝐈𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐏𝐮𝐳𝐳𝐥𝐞 Suppose we train a hard-margin linear SVM on n > 100 data points in R^2, yielding a hyperplane with exactly 2 support vectors. If we add one more data point and retrain the classifier, what is the maximum possible number of support vectors for the new hyperplane (assuming the n+1 points are linearly separable)? Choose the correct answer. A) 2 B) n C) 3 D) n+1

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What do you need to successfully deploy LLMs in your enterprise? Andrei Dmitrenko, Carlos Parés Pulido and Meret Ringwald are running the workshop “Building Agile Data Foundations for Data Mesh & Generative AI” at the Swiss Conference on Data Science | SDS Find more information here: https://lnkd.in/d_3fXhqc #LLMs #DataVault #SDS2024 #IEEEConference #doneai

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That's awesome! Mistral's new features for building custom LLMs sound incredibly developer-friendly. The ability to fine-tune and create custom agents makes it a powerful tool for personalized AI solutions. Thanks for sharing the video link—can't wait to check it out! 🚀🔍 #CustomLLMs #AIInnovation #DeveloperTools #MachineLearning #MistralLLM

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A great read on the performance and financials of LLM fine-tuning. Really hoping the outcome is a tool that helps overcome the biggest elephant in the room, Mr. Cost! #llm #finetuning #finance #computaioncosts

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The new Cyclic partitioning method not only optimizes value assignments but can potentially accelerate algorithms like QuickSort and median-finding by up to 1.5 times when dealing with complex data types. Read more in Tigran Hayrapetyan's latest article. #ComputerScience

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🚀 Want to employ the power of #PyTorch for your scientific models without a steep learning curve? 👀 Check out our latest blog, where Travis Oliphant discusses how PyTorch's integration with the Array API makes it easy to use with familiar libraries like NumPy and scikit-learn.🔥 https://lnkd.in/dDRkEY3v #Innovation

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The ASTx project, a component of the ArxLang ecosystem, serves as an agnostic Python framework for constructing and representing Abstract Syntax Trees (ASTs). Its primary objective is to provide a versatile and language-independent structure for ASTs, primarily catering to the needs of the ArxLang project but with the flexibility to be utilized across various programming languages and parsing tools. ASTx supports various data types such as integer, float and boolean, and features like control flow elements and operators. When it comes to visualization, ASTx supports Graphviz and console-based visualization. Both are very helpful for graphical interpretation and debugging, but the ASCII-based console visualization has added versatility for use during a pdb session, for example. With all these features, ASTx is an intuitive and powerful interface that can be used to create custom code analysis tools or develop your own language. Want to know more about this intuitive tool? Join Ana Paula Morais Krelling at the PyLadiesCon 2024. 😊 Time: 2024-12-07 01:10–01:30 (UTC) / 20:10-20:30 (America/New_York) #PyLadiesCon2024 #SoftwareDevelopment #Python #Coding #ASTx #ASTs

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Large Generative Graph Models (LGGMs): A New Class of Graph Generative Model Trained on a Large Corpus of Graphs Quick read: https://lnkd.in/gt_GbngB Paper: https://lnkd.in/gP27y6Gq

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Economics of Hosting Open Source LLMs. As LLM usage has skyrocketed in the past year or so, practitioners have increasingly asked themselves what the most efficient way to deploy these models might be. Ida Silfverskiöld offers a detailed breakdown of the various factors to consider and how different providers stack up when it comes to processing time, cold start delays, and CPU, memory, and GPU costs. https://buff.ly/4hIiocP #OpenSource #LLMs #AIInfrastructure #TechEconomics #AIInnovation

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"In particular, I was interested in two things: 1. How Polars compares to pandas, with only 1 vCore available 2. How Polars scales with vCores" Saar Berkovich provides a nuanced comparison of Pandas 2 and Polars.

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Thank you, Sion Smith, for having us! 🎙 In this talk, Yingjun Wu introduces RisingWave, a scalable streaming database that utilizes #S3 as its primary storage. From underlying technology to real-world scenarios, Yingjun illustrates how RisingWave supports heavy traffic workloads in enterprise-grade production environments. #risingwave #streamingdatabase

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🤔 Facing challenges with traditional Retrieval-Augmented Generation systems in your LLM projects? 🚀 Introducing GraphRAG: A Solution to Power Up LLMs with Knowledge Graphs: https://lnkd.in/gUpyKgSy Traditional retrieval systems often struggle with the complexity and volume of data, leading to suboptimal integration of information into LLMs. GraphRAG, developed by Microsoft Research, revolutionizes this by leveraging knowledge graphs to enhance data structuring, relevance, and retrieval efficiency. 📅 Join us at the DataHack Summit 2024 for an in-depth session: Topic: "GraphRAG: Powering Up LLMs with Knowledge Graphs" The speaker of this session will be Siddhant Agarwal, Developer Relations Lead - APAC at Neo4j. In this session, Siddhant will explore how GraphRAG addresses these challenges by: ➡️ Creating Knowledge Graphs: Extract entities and relationships to form a structured knowledge base. ➡️ Semantic Clustering: Organize the graph into semantic communities for better data cohesion. ➡️ Enhanced Query Processing: Generate comprehensive, diverse answers relevant to user queries. #AnalyticsVidhya #DataHackSummit2024 #DHS2024

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📣 New in MEAP! 📣 Knowledge Graph-Enhanced RAG, by Tomaz Bratanic and Oskar Hane. 📚 Have you ever considered incorporating structured data into your #RAG applications? While most people focus solely on unstructured data as the primary source for RAG, you can also utilize #structureddata. Our latest MEAP release, "Knowledge Graph-Enhanced RAG," provides practical techniques to help you build RAG using both unstructured and structured data. This book is a practical guide to empowering #LLMs with RAG. Throughout the book, you will learn how and why storing data as a graph can enhance RAG accuracy, performance, and traceability. You will also learn to build a vector similarity search retrieval tool, create an Agentic RAG application, extract information from text to form a #KnowledgeGraph, evaluate performance and accuracy, and much more. 🚀 Take action now! Save 50% with code libratanic350. 📖 Get into the book: https://mng.bz/4JNB 📹 Check out this video to find out: https://mng.bz/v87x #GraphRAG #ManningBooks #LearnwithManning

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How can reranking models improve your RAG pipeline? This article by Daniel Klitzke explains how to integrate a reranking model with Huggingface Transformers to boost context quality. #RAG #LLM

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Learn the best strategies for selecting a group of nodes with the highest combined influence on the graph — Eivind Kjosbakken provides a clear and detailed explanation in his in-depth tutorial.

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Learn to build an automated research paper report generator using LlamaIndex that downloads papers from arXiv, processes them with LlamaParse, and indexes them in LlamaCloud! Report generation is an increasingly important use-case so we're doubling down on making it easy. In this demo from Ravi Theja Desetty you'll learn how to: ➡️ Generate queries based on a report outline and classify them for LLM vs index-based answers ➡️ Handle paper downloading, metadata extraction, and document parsing automatically ➡️ Use Pydantic models to extract key metadata like author names, companies, and AI topics ➡️ Create structured reports with sections & subsections filled from both LLM and indexed content It supports customizable report outlines and templates for different use cases. Check it out here: https://lnkd.in/gCXKzeuH

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