mailitics

Tag: using

  • What should I tell the students about job opportunities?

    What should I tell the students about job opportunities? I am a data scientist with almost two years of experience. I mainly work on SQL, Pandas, Power BI dashboards, credit risk modeling, MLOps, and a small part of GenAI architecture using Redis workers. I have been invited to my college, where I completed my Masters…

  • Using Local LLMs to Discover High-Performance Algorithms

    Using Local LLMs to Discover High-Performance Algorithms How I used open-source models to explore new frontiers in efficient code generation, using my MacBook and local LLMs. The post Using Local LLMs to Discover High-Performance Algorithms appeared first on Towards Data Science. Stefano Bosisio Go to original source

  • Using Python to Build a Calculator

    Using Python to Build a Calculator A beginner-friendly Python project to understand conditional statements, loops and recursive functions The post Using Python to Build a Calculator appeared first on Towards Data Science. Mahnoor Javed Go to original source

  • Using LangGraph and MCP Servers to Create My Own Voice Assistant

    Using LangGraph and MCP Servers to Create My Own Voice Assistant Built over 14 days, all locally run, no API keys, cloud services, or subscription fees. The post Using LangGraph and MCP Servers to Create My Own Voice Assistant appeared first on Towards Data Science. Benjamin Lee Go to original source

  • Determination of Particle-Size Distributions from Light-Scattering Measurement Using Constrained Gaussian Process Regression

    Determination of Particle-Size Distributions from Light-Scattering Measurement Using Constrained Gaussian Process Regression arXiv:2507.03736v1 Announce Type: new Abstract: In this work, we propose a novel methodology for robustly estimating particle size distributions from optical scattering measurements using constrained Gaussian process regression. The estimation of particle size distributions is commonly formulated as a Fredholm integral equation of…

  • Estimating Product-Level Price Elasticities Using Hierarchical Bayesian

    Estimating Product-Level Price Elasticities Using Hierarchical Bayesian Using one model to personalize ML results The post Estimating Product-Level Price Elasticities Using Hierarchical Bayesian appeared first on Towards Data Science. Derek Tran Go to original source

  • Lie Group Symmetry Discovery and Enforcement Using Vector Fields

    Lie Group Symmetry Discovery and Enforcement Using Vector Fields arXiv:2505.08219v1 Announce Type: new Abstract: Symmetry-informed machine learning can exhibit advantages over machine learning which fails to account for symmetry. Additionally, recent attention has been given to continuous symmetry discovery using vector fields which serve as infinitesimal generators for Lie group symmetries. In this paper, we…

  • Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data

    Data Pruning MNIST: How I Hit 99% Accuracy Using Half the Data How much data does AI really need? TLDR: Data-centric AI can create more efficient and accurate models. I experimented with data pruning on MNIST¹ to classify handwritten digits. Best runs for “furthest-from-centroid” selection compared to full dataset. Image by author. What if I told you…

  • Linearizing Llama

    Linearizing Llama Speeding up Llama: A hybrid approach to attention mechanisms Source: Image by Author (Generated using Gemini 1.5 Flash) In this article, we will see how to replace softmax self-attention in Llama-3.2-1B with hybrid attention combining softmax sliding window and linear attention. This implementation will help us better understand the growing interest in linear attention…

  • Deep learning joint extremes of metocean variables using the SPAR model

    Deep learning joint extremes of metocean variables using the SPAR model arXiv:2412.15808v1 Announce Type: new Abstract: This paper presents a novel deep learning framework for estimating multivariate joint extremes of metocean variables, based on the Semi-Parametric Angular-Radial (SPAR) model. When considered in polar coordinates, the problem of modelling multivariate extremes is transformed to one of…