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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · Towards AI English(EN) · · [2 sources]

    SVM : 40 Must Visit Interview Questions (Part 1)

    This article series delves into Support Vector Machines (SVMs), a popular machine learning algorithm, by presenting a comprehensive list of interview-style questions. Part 1 covers foundational concepts like decision boundaries, hyperplanes, and the intuition behind maximizing margins, along with distinctions between hard-margin and soft-margin classifiers. Part 2 builds on this by exploring the kernel trick, its power, different kernel types, and challenges, as well as how SVMs handle multi-class problems and compare to other algorithms like Logistic Regression. AI

    SVM : 40 Must Visit Interview Questions (Part 1)

    IMPACT Provides foundational knowledge for machine learning practitioners and students preparing for interviews on core algorithms.

  2. COMMENTARY · Medium — MLOps tag English(EN) ·

    Beyond the Jupyter Notebook: Building a Production-First Data Science Portfolio for 2026

    The article discusses the evolving landscape of data science portfolios, emphasizing a shift towards production-ready applications over traditional Jupyter Notebooks. It highlights the need for data scientists to demonstrate skills in deploying and managing models in real-world scenarios, particularly with the abundance of unstructured data in enterprises. The author suggests that a portfolio focused on production-first projects will be crucial for career success in 2026. AI

    IMPACT Data scientists need to adapt their portfolios to showcase production deployment skills for future job market relevance.

  3. RESEARCH · Mastodon — mastodon.social English(EN) · · [10 sources]

    A new batch of modules in the Statistics Globe Hub is about to start. You can find more information about the Statistics Globe Hub, along with the full list of

    Two recent surveys explore the application of AI and deep learning in distinct fields. One paper focuses on explainable AI for detecting mental disorders through social media, emphasizing the need for transparency in healthcare AI. Another survey reviews deep learning techniques for crops, fisheries, and livestock, highlighting challenges and future directions like multimodal data integration and edge-device deployment. Additionally, several articles discuss the distinctions between AI, Machine Learning, and Deep Learning, often with practical Python examples, while others highlight AI's role in agriculture and data science education. AI

    A new batch of modules in the Statistics Globe Hub is about to start. You can find more information about the Statistics Globe Hub, along with the full list of

    IMPACT Clarifies distinctions between AI, ML, and DL, and surveys their applications in mental health and agriculture.