In this tutorial, we explore the advanced capabilities of PyGWalker, a powerful tool for visual data analysis that integrates seamlessly with pandas. We begin by generating a realistic e-commerce dataset enriched with time, demographic, and marketing features to mimic real-world business data. We then prepare multiple analytical views, including daily sales, category performance, and customer [&#8230;]
The post <a href="https://www.marktechpost.com/2025/11/11/how-to-build-an-end-to-end-interactive-analytics-dashboard-using-pygwalker-features-for-insightful-data-exploration/">How to Build an End-to-End Interactive Analytics Dashboard Using PyGWalker Features for Insightful Data Exploration</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.

تم نشر دليل في 12 نوفمبر 2025 يوضح كيفية إنشاء لوحة تحكم تحليلية تفاعلية باستخدام PyGWalker، وهي أداة مصممة لتحليل البيانات بصريًا. يركز الدليل على مجموعة بيانات التجارة الإلكترونية التي تتضمن ميزات زمنية وديموغرافية وتسويقية، مما يسمح للمستخدمين باستكشاف وجهات نظر تحليلية متنوعة مثل المبيعات اليومية وأداء الفئات. تعتبر هذه المورد مهمًا لمحللي البيانات الذين يسعون لتحسين قدراتهم في استكشاف البيانات.

Se publicó un tutorial el 12 de noviembre de 2025, que detalla cómo crear un panel de análisis interactivo utilizando PyGWalker, una herramienta diseñada para el análisis visual de datos. El tutorial se centra en un conjunto de datos de comercio electrónico que incluye características temporales, demográficas y de marketing, permitiendo a los usuarios explorar diversas vistas analíticas como ventas diarias y rendimiento de categorías. Este recurso es significativo para los analistas de datos que buscan mejorar sus capacidades de exploración de datos.

Un tutoriel a été publié le 12 novembre 2025, détaillant comment créer un tableau de bord analytique interactif en utilisant PyGWalker, un outil conçu pour l'analyse visuelle des données. Le tutoriel se concentre sur un ensemble de données e-commerce comprenant des caractéristiques temporelles, démographiques et marketing, permettant aux utilisateurs d'explorer diverses vues analytiques telles que les ventes quotidiennes et la performance des catégories. Cette ressource est significative pour les analystes de données cherchant à améliorer leurs capacités d'exploration de données.

A tutorial was published on November 12, 2025, detailing how to create an interactive analytics dashboard using PyGWalker, a tool designed for visual data analysis. The tutorial focuses on an e-commerce dataset that includes time, demographic, and marketing features, allowing users to explore various analytical views such as daily sales and category performance. This resource is significant for data analysts seeking to enhance their data exploration capabilities.

How to Build an End-to-End Interactive Analytics Dashboard Using PyGWalker Features for Insightful Data Exploration

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<h1>
 
 
 AI Amnesia: Erasing Knowledge Without a Trace
</h1>

Imagine your AI model accidentally learned something it shouldn't have – sensitive customer data, for example. Current methods for deleting this information often require retraining the entire model, an expensive and time-consuming process. What if we could surgically remove that knowledge without starting from scratch?

The key lies in a novel approach: creating artificial "forgetting cues." We're teaching the model to unlearn specific data patterns by exposing it to carefully crafted synthetic examples. These examples are designed to strongly contradict the information we want the model to forget, effectively overwriting the problematic associations in its memory. This works even if you don’t have access to the original data you need to erase.

Think of it like this: you're trying to forget a bad song stuck in your head. Instead of trying to actively suppress it (which rarely works), you blast an even more catchy song. The new song overwrites the old one, effectively erasing it from your mental playlist.

Benefits of Selective Forgetting:

<ul>
<li> Enhanced Data Privacy: Remove sensitive data without compromising the overall model's performance.</li>
<li> Reduced Retraining Costs: Avoid full model retraining, saving significant time and resources.</li>
<li> Improved Model Security: Eliminate vulnerabilities introduced by unintentionally learned patterns.</li>
<li> Adaptable Learning: Enables continuous refinement of AI models based on evolving data landscapes.</li>
<li> Compliance Ready: Supports compliance with data privacy regulations like GDPR.</li>
<li> Scalable Solutions: Works efficiently even with limited access to training data.</li>
</ul>

Practical Tip: One challenge is ensuring the synthetic data accurately targets the information you want to remove without negatively impacting the model's ability to generalize. Rigorous testing and validation with holdout datasets are crucial.

The promise of AI that can truly 'forget' opens exciting possibilities for responsible AI development. By enabling precise data deletion, we pave the way for more secure, compliant, and adaptable machine learning systems. Imagine AI models that can adapt to changing ethical guidelines or quickly unlearn incorrect information, all without massive retraining efforts. This is a crucial step towards trustworthy and responsible AI that respects data privacy and aligns with societal values. Future exploration could include extending this to different data modalities and model architectures.

Related Keywords: machine unlearning, data privacy, few-shot learning, zero-shot learning, synthetic data, model editing, catastrophic forgetting, incremental learning, continual learning, deep learning, neural networks, data security, algorithmic fairness, responsible ai, ethical ai, federated unlearning, privacy-preserving ai, model retraining, AI governance, data deletion, GDPR compliance

يتناول المقال نهجًا جديدًا لمعالجة مشكلة نسيان الذكاء الاصطناعي، حيث قد تحتفظ نماذج الذكاء الاصطناعي عن غير قصد بمعلومات حساسة. تتطلب الطرق التقليدية لحذف هذه البيانات غالبًا إعادة تدريب كاملة للنموذج، وهو ما يعد مكلفًا ويستغرق وقتًا. تتضمن التقنية الجديدة إنشاء 'إشارات نسيان' اصطناعية تساعد النموذج على نسيان أنماط بيانات معينة من خلال تعريضه لأمثلة صناعية تتناقض مع المعلومات غير المرغوب فيها، مما يسمح بإزالة المعرفة المستهدفة دون الحاجة للوصول إلى البيانات الأصلية.

El artículo aborda un nuevo enfoque para tratar el problema de la amnesia de la IA, donde los modelos de IA pueden retener inadvertidamente información sensible. Los métodos tradicionales para eliminar estos datos a menudo requieren un reentrenamiento completo del modelo, lo que es costoso y lleva tiempo. La nueva técnica implica crear 'señales de olvido' artificiales que ayudan al modelo a desaprender patrones de datos específicos al exponerlo a ejemplos sintéticos que contradicen la información no deseada, permitiendo así la eliminación selectiva de conocimientos sin necesidad de acceder a l…

L'article traite d'une nouvelle approche pour résoudre le problème de l'amnésie de l'IA, où les modèles d'IA peuvent involontairement conserver des informations sensibles. Les méthodes traditionnelles pour supprimer ces données nécessitent souvent un réentraînement complet du modèle, ce qui est coûteux et long. La nouvelle technique consiste à créer des 'cues d'oubli' artificiels qui aident le modèle à désapprendre des schémas de données spécifiques en lui présentant des exemples synthétiques qui contredisent les informations indésirables, permettant ainsi une suppression ciblée des connaissan…

The article discusses a novel approach to address the issue of AI amnesia, where AI models may inadvertently retain sensitive information. Traditional methods for deleting such data often require complete retraining of the model, which is costly and time-consuming. The new technique involves creating artificial 'forgetting cues' that help the model unlearn specific data patterns by presenting it with synthetic examples that contradict the unwanted information, allowing for targeted knowledge removal without needing access to the original data.

AI Amnesia: Erasing Knowledge Without a Trace

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Turning materials like wood chips, crop residues and municipal solid waste into fuels and chemicals is important for our country's energy independence.

طور الباحثون نماذج حاسوبية متقدمة تهدف إلى تحسين التنبؤات لعمليات طحن الكتلة الحيوية. تركز هذه الابتكارات على تحويل مواد مثل رقائق الخشب، بقايا المحاصيل، والنفايات الصلبة البلدية إلى وقود ومواد كيميائية قيمة، وهو أمر مهم لتعزيز استقلالية الطاقة في البلاد. من المتوقع أن تعمل النماذج على تحسين كفاءة معالجة الكتلة الحيوية، مما يساهم في حلول الطاقة المستدامة.

Investigadores han desarrollado modelos informáticos avanzados destinados a mejorar las predicciones para los procesos de molienda de biomasa. Esta innovación se centra en convertir materiales como astillas de madera, residuos de cultivos y residuos sólidos municipales en combustibles y productos químicos valiosos, lo que es crucial para mejorar la independencia energética del país. Se espera que los modelos optimicen la eficiencia del procesamiento de biomasa, contribuyendo así a soluciones energéticas sostenibles.

Des chercheurs ont développé des modèles informatiques avancés visant à améliorer les prévisions pour les processus de broyage de la biomasse. Cette innovation se concentre sur la conversion de matériaux tels que les copeaux de bois, les résidus de culture et les déchets solides municipaux en combustibles et produits chimiques précieux, ce qui est crucial pour renforcer l'indépendance énergétique du pays. Les modèles devraient optimiser l'efficacité du traitement de la biomasse, contribuant ainsi à des solutions énergétiques durables.

Researchers have developed advanced computer models aimed at improving predictions for biomass milling processes. This innovation focuses on converting materials such as wood chips, crop residues, and municipal solid waste into valuable fuels and chemicals, which is crucial for enhancing energy independence in the country. The models are expected to optimize the efficiency of biomass processing, thereby contributing to sustainable energy solutions.

Researchers develop computer models for better biomass milling predictions

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arXiv:2511.11305v1 Announce Type: cross 
Abstract: We introduce MOON, our comprehensive set of sustainable iterative practices for multimodal representation learning for e-commerce applications. MOON has already been fully deployed across all stages of Taobao search advertising system, including retrieval, relevance, ranking, and so on. The performance gains are particularly significant on click-through rate (CTR) prediction task, which achieves an overall +20.00% online CTR improvement. Over the past three years, this project has delivered the largest improvement on CTR prediction task and undergone five full-scale iterations. Throughout the exploration and iteration of our MOON, we have accumulated valuable insights and practical experience that we believe will benefit the research community. MOON contains a three-stage training paradigm of "Pretraining, Post-training, and Application", allowing effective integration of multimodal representations with downstream tasks. Notably, to bridge the misalignment between the objectives of multimodal representation learning and downstream training, we define the exchange rate to quantify how effectively improvements in an intermediate metric can translate into downstream gains. Through this analysis, we identify the image-based search recall as a critical intermediate metric guiding the optimization of multimodal models. Over three years and five iterations, MOON has evolved along four critical dimensions: data processing, training strategy, model architecture, and downstream application. The lessons and insights gained through the iterative improvements will also be shared. As part of our exploration into scaling effects in the e-commerce field, we further conduct a systematic study of the scaling laws governing multimodal representation learning, examining multiple factors such as the number of training tokens, negative samples, and the length of user behavior sequences.

MOON هو مجموعة شاملة من الممارسات التكرارية المستدامة لتعلم التمثيلات متعددة الوسائط في تطبيقات التجارة الإلكترونية. تم نشره بالكامل عبر جميع مراحل نظام إعلانات البحث في تاوباو، وقد حقق MOON تحسينًا كبيرًا بنسبة 20% في توقع معدل النقر. على مدار السنوات الثلاث الماضية، خضع المشروع لخمس دورات كاملة، مما وفر رؤى قيمة وتجارب عملية للمجتمع البحثي.

MOON es un conjunto integral de prácticas iterativas sostenibles para el aprendizaje de representaciones multimodales en aplicaciones de comercio electrónico. Totalmente implementado en todas las etapas del sistema de publicidad de búsqueda de Taobao, MOON ha mejorado significativamente la predicción de la tasa de clics (CTR) en un 20%. El proyecto ha pasado por cinco iteraciones en los últimos tres años, acumulando valiosas ideas y experiencia práctica para la comunidad de investigación.

MOON est un ensemble complet de pratiques itératives durables pour l'apprentissage de représentations multimodales dans les applications de commerce électronique. Déployé dans toutes les étapes du système de publicité de recherche Taobao, MOON a considérablement amélioré la prédiction du taux de clics (CTR) de 20 %. Le projet a subi cinq itérations au cours des trois dernières années, fournissant des informations précieuses et une expérience pratique à la communauté de recherche.

MOON is a comprehensive set of sustainable iterative practices for multimodal representation learning in e-commerce applications. Fully deployed across all stages of the Taobao search advertising system, MOON has significantly improved click-through rate (CTR) prediction by 20%. The project has undergone five iterations over three years, providing valuable insights and practical experience for the research community.

MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising

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The post <a href="https://www.marktechpost.com/2025/11/18/how-to-build-an-agentic-deep-reinforcement-learning-system-with-curriculum-progression-adaptive-exploration-and-meta-level-ucb-planning/">How to Build an Agentic Deep Reinforcement Learning System with Curriculum Progression, Adaptive Exploration, and Meta-Level UCB Planning</a> appeared first on <a href="https://www.marktechpost.com">MarkTechPost</a>.

How to Build an Agentic Deep Reinforcement Learning System with Curriculum Progression, Adaptive Exploration, and Meta-Level UCB Planning

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How to Build an End-to-End Interactive Analytics Dashboard Using PyGWalker Features for Insightful Data Exploration

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