Filippo Todeschini

Anthropic introduced Artifacts, which allows users to work on generated outputs as independent files on Claude. Find more details in #TheBatch: https://hubs.la/Q02GkffD0

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Learn how to fine-tune and evaluate Mistral AI models on serverless GPUs 🚀 This tutorial showcases how to fine-tune the Mistral 7B model, but you can use the same steps to fine-tune other Mistral models ✨ https://lnkd.in/d3HptaxX

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🐍📺 Simplify Python GUI Development With PySimpleGUI [Video] In this step-by-step course, you'll learn how to create a cross-platform graphical user interface (GUI) using Python and PySimpleGUI #python

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Vector databases store and query data differently than traditional relational databases, with vector indexes built on proximity in high-dimensional space lacking strict ordering. Querying vector indexes relies on approximate spatial navigation and topk algorithms rather than ordered scans. While scalar databases support ordered index scans, vector indexes exhibit relaxed monotonicity enabling early query termination without strict ordering. The VBase system provides a unified foundation for efficiently scanning vector and scalar indexes through a single interface and shared early termination conditions. It improves query performance in vector databases by 10 to 1000 times and increases accuracy. VBase enables building databases capable of complex relational queries across vector and scalar data. SPFresh is the first vector index supporting real-time, incremental updates by dynamically splitting or merging partitions and reassigning vectors using a lightweight rebalancing protocol. Vector databases are widely applied in natural language processing, information retrieval, and recommendations, providing efficient handling of unstructured data in knowledge expansion. https://lnkd.in/ekH9UC9j

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PyTorch 2.3 is here 😎🔥 PyTorch 2.3 offers support for user-defined Triton kernels in torch.compile, allowing for users to migrate their own Triton kernels from eager without experiencing performance regressions or graph breaks. Details: https://hubs.la/Q02tY8PB0

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In a beginner-friendly primer, Vitor Cerqueira lays out the basics of concept drift and its relevance for the performance of AI-based systems.

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This repository by Nir Diamant offers a wide range of strategies for RAG (currently 17), along with tutorials and visualizations, making it an excellent resource for learning and reference. https://lnkd.in/gia_ktaF #python #Programming #PythonProgramming #DataScience #MachineLearning #SoftwareDevelopment #WebDevelopment #TechNews #OpenSource #DataEngineering

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Building Advanced RAG Systems: From Basic to Agentic! What's New in RAG? Traditional RAG is evolving into Agentic RAG, combining retrieval capabilities with intelligent decision-making frameworks Key Components of Modern RAG Core ArchitectureMulti-agent frameworks for enhanced decision-making Tool-calling capabilities with Llama 3.1 NVIDIA NeMo Retriever integration Business Benefits Improved accuracy with multi-step reasoning Scalable deployment options Enhanced data security Flexible integration capabilities Implementation Features Query decomposition for complex questions Intelligent routing systems Hallucination checking Post-generation verification #AI #RAG #MachineLearning #NVIDIA #DataScience #Innovation

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🚀 Multivariate Forecasting Model 🚀 Forecasting models often struggle with channel independence and distribution drift in complex data. The SOFTS model, ensures accurate and efficient forecasting in dynamic environments. Paper 🔗 - https://lnkd.in/d9VQtmQG 📧 Excited about AI innovations? DM us to learn more!

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How can generative AI tools represent less common identities and narratives; Can LLMs help players participate in game narratives; Using LLMs to improve geospatial demographic data; A Graph RAG Approach to Query-Focused Summarization; and more. https://msft.it/6044Yi6Na

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⭐️ New video release 📺: Data valuation for machine learning Watch as Miguel de Benito Delgado and Kristof Schröder discuss the importance of data valuation techniques in machine learning and demonstrate how the open-source library pyDVL can enhance data quality and model performance effortlessly. 📺 Watch the video on YouTube: https://lnkd.in/eipKzj2f In a talk about data valuation for machine learning, Miguel de Benito Delgado, an applied researcher at the appliedAI Initiative GmbH, and Kristof Schröder from the TransferLab team at appliedAI Institute for Europe gGmbH Institute discussed the significance of determining the value of training data points in machine learning models. The core idea of data-centric machine learning emphasizes the importance of enhancing data quality over model improvement, especially in scenarios with limited or costly data. They introduced the concept of data valuation as the process of assigning a value to each element in a training set based on its impact on the model's performance. By using the open-source library pyDVL, they showcased how to identify mislabeled or out-of-distribution samples efficiently. The talk highlighted the practical implications of data valuation in data engineering, model debugging, and development processes, emphasizing the relevance of focusing on valuable data points while eliminating less useful ones. The speakers also discussed the evolution of data valuation methods and recent advances in the field, addressing strategies for repairing or pruning corrupt data and optimizing data collection through techniques like active learning. pyDVL, a library designed for robust data valuation implementations, was presented as a valuable tool for detecting data pipeline issues and enhancing model performance in machine learning applications.

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Implement Tensor Operations With PyTorch einsum: Basic to Self-attention via #TowardsAI → https://bit.ly/3zpy9Uc

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Implement Tensor Operations With PyTorch einsum: Basic to Self-attention via #TowardsAI → https://bit.ly/3zpy9Uc

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Explore José Antonio Siqueira de Cerqueira's efforts in enhancing language models for greater safety and reliability. Dive into this article to discover his significant contributions to the field - https://lnkd.in/dA-EWMu2! 📚👨‍💻 #research #languagemodels

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❓How to Improve LLMs with RAG? ☕️Grab a coffee, and 👀check this awesome video by Shaw Talebi that provides a high-level introduction to RAG and shares the example Python code for implementing an RAG system using LlamaIndex. https://lnkd.in/gGtHFpmt #LLM #python #RAG #MyScaleDB

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