📌 When Things Get Weird with Custom Calendars in Tabular Models
🗂 Category: POWER BI
🕒 Date: 2026-04-10 | ⏱️ Read time: 10 min read
Since September 2025, we have had Calendar-based Time Intelligence in Power BI and Fabric Tabular…
#DataScience #AI #Python
🗂 Category: POWER BI
🕒 Date: 2026-04-10 | ⏱️ Read time: 10 min read
Since September 2025, we have had Calendar-based Time Intelligence in Power BI and Fabric Tabular…
#DataScience #AI #Python
❤1
📌 Advanced RAG Retrieval: Cross-Encoders & Reranking
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-04-11 | ⏱️ Read time: 28 min read
A deep-dive and practical guide to cross-encoders, advanced techniques, and why your retrieval pipeline deserves…
#DataScience #AI #Python
🗂 Category: LLM APPLICATIONS
🕒 Date: 2026-04-11 | ⏱️ Read time: 28 min read
A deep-dive and practical guide to cross-encoders, advanced techniques, and why your retrieval pipeline deserves…
#DataScience #AI #Python
📌 Why Every AI Coding Assistant Needs a Memory Layer
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-11 | ⏱️ Read time: 10 min read
AI coding assistants need a persistent memory layer to overcome the statelessness of LLMs and…
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-11 | ⏱️ Read time: 10 min read
AI coding assistants need a persistent memory layer to overcome the statelessness of LLMs and…
#DataScience #AI #Python
📌 Introduction to Reinforcement Learning Agents with the Unity Game Engine
🗂 Category: REINFORCEMENT LEARNING
🕒 Date: 2026-04-11 | ⏱️ Read time: 10 min read
A step-by-step interactive guide to one of the most vexing areas of machine learning.
#DataScience #AI #Python
🗂 Category: REINFORCEMENT LEARNING
🕒 Date: 2026-04-11 | ⏱️ Read time: 10 min read
A step-by-step interactive guide to one of the most vexing areas of machine learning.
#DataScience #AI #Python
📌 Your ReAct Agent Is Wasting 90% of Its Retries — Here’s How to Stop It
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-12 | ⏱️ Read time: 19 min read
Most ReAct-style agents are silently wasting their retry budget on errors that can never succeed.…
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-12 | ⏱️ Read time: 19 min read
Most ReAct-style agents are silently wasting their retry budget on errors that can never succeed.…
#DataScience #AI #Python
❤1
📌 Stop Treating AI Memory Like a Search Problem
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-12 | ⏱️ Read time: 22 min read
Why storing and retrieving data isn’t enough to build reliable AI memory systems
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-12 | ⏱️ Read time: 22 min read
Why storing and retrieving data isn’t enough to build reliable AI memory systems
#DataScience #AI #Python
❤1
📌 Write Pandas Like a Pro With Method Chaining Pipelines
🗂 Category: PROGRAMMING
🕒 Date: 2026-04-12 | ⏱️ Read time: 15 min read
Master method chaining, assign(), and pipe() to write cleaner, testable, production-ready Pandas code
#DataScience #AI #Python
🗂 Category: PROGRAMMING
🕒 Date: 2026-04-12 | ⏱️ Read time: 15 min read
Master method chaining, assign(), and pipe() to write cleaner, testable, production-ready Pandas code
#DataScience #AI #Python
Forwarded from ML Research Hub
Exploring the Future of AI: Neutrosophic Graph Neural Networks (NGNN)
Recent analysis indicates that Neutrosophic Graph Neural Networks (NGNN) represent a significant advancement in contemporary artificial intelligence research. The following overview details the concept and its implications.
Most artificial intelligence models presuppose data integrity; however, real-world data is frequently imperfect. Consequently, NGNN may emerge as a critical innovation.
The foundational inquiry addresses the following:
How does artificial intelligence manage data characterized by uncertainty, incompleteness, or contradiction?
Traditional models exhibit limitations in this regard, often assuming certainty where none exists.
The Foundation: Neutrosophic Logic
In the late 1990s, mathematician Florentin Smarandache introduced a framework extending beyond binary true/false dichotomies. He proposed three dimensions of truth:
T — What is true
I — What is indeterminate
F — What is false
Between 2000 and 2015, this framework evolved into neutrosophic sets and neutrosophic graphs, mathematical tools capable of encoding uncertainty within data and relationships.
The Parallel Rise of Graph Neural Networks
Around 2016, the artificial intelligence sector adopted Graph Neural Networks (GNNs), models designed to learn from nodes (data points) and edges (relationships). These models became foundational in social networks, healthcare, fraud detection, and bioinformatics.
However, GNNs possess a critical limitation: they assume data certainty, whereas real-world data is inherently uncertain.
The Convergence: NGNN
From 2020 onwards, researchers began integrating these two domains. In an NGNN, rather than carrying only features, a node encapsulates:
— T: What is likely true
— I: What remains uncertain
— F: What may be false
This constitutes not a minor upgrade, but a fundamental shift in how artificial intelligence models perceive and process reality.
Key Application Areas:
Healthcare — Navigating uncertain or conflicting diagnoses
Fraud detection — Identifying ambiguous behavioral patterns
Social networks — Modeling unclear or evolving relationships
Bioinformatics — Managing the complexity of biological interactions
Is NGNN advanced machine learning?
Affirmatively. It resides at the intersection of:
Graph theory · Deep learning · Mathematical logic · Uncertainty modeling
This technology represents research-level, cutting-edge development and is not yet widely deployed in industry. This status underscores its current strategic importance.
The Broader Context
NGNN is not merely another model; it signifies a philosophical shift in artificial intelligence from systems assuming certainty to systems reasoning through uncertainty. Real-world problems are rarely perfect; therefore, models should not presume perfection.
This represents not only evolution but a definitive direction for the field.
——
#ArtificialIntelligence #MachineLearning #DeepLearning #GraphNeuralNetworks #AIResearch #DataScience #FutureOfAI #Innovation #EmergingTech #NGNN #AIHealthcare #Bioinformatics
Recent analysis indicates that Neutrosophic Graph Neural Networks (NGNN) represent a significant advancement in contemporary artificial intelligence research. The following overview details the concept and its implications.
Most artificial intelligence models presuppose data integrity; however, real-world data is frequently imperfect. Consequently, NGNN may emerge as a critical innovation.
The foundational inquiry addresses the following:
How does artificial intelligence manage data characterized by uncertainty, incompleteness, or contradiction?
Traditional models exhibit limitations in this regard, often assuming certainty where none exists.
The Foundation: Neutrosophic Logic
In the late 1990s, mathematician Florentin Smarandache introduced a framework extending beyond binary true/false dichotomies. He proposed three dimensions of truth:
T — What is true
I — What is indeterminate
F — What is false
Between 2000 and 2015, this framework evolved into neutrosophic sets and neutrosophic graphs, mathematical tools capable of encoding uncertainty within data and relationships.
The Parallel Rise of Graph Neural Networks
Around 2016, the artificial intelligence sector adopted Graph Neural Networks (GNNs), models designed to learn from nodes (data points) and edges (relationships). These models became foundational in social networks, healthcare, fraud detection, and bioinformatics.
However, GNNs possess a critical limitation: they assume data certainty, whereas real-world data is inherently uncertain.
The Convergence: NGNN
From 2020 onwards, researchers began integrating these two domains. In an NGNN, rather than carrying only features, a node encapsulates:
— T: What is likely true
— I: What remains uncertain
— F: What may be false
This constitutes not a minor upgrade, but a fundamental shift in how artificial intelligence models perceive and process reality.
Key Application Areas:
Healthcare — Navigating uncertain or conflicting diagnoses
Fraud detection — Identifying ambiguous behavioral patterns
Social networks — Modeling unclear or evolving relationships
Bioinformatics — Managing the complexity of biological interactions
Is NGNN advanced machine learning?
Affirmatively. It resides at the intersection of:
Graph theory · Deep learning · Mathematical logic · Uncertainty modeling
This technology represents research-level, cutting-edge development and is not yet widely deployed in industry. This status underscores its current strategic importance.
The Broader Context
NGNN is not merely another model; it signifies a philosophical shift in artificial intelligence from systems assuming certainty to systems reasoning through uncertainty. Real-world problems are rarely perfect; therefore, models should not presume perfection.
This represents not only evolution but a definitive direction for the field.
——
#ArtificialIntelligence #MachineLearning #DeepLearning #GraphNeuralNetworks #AIResearch #DataScience #FutureOfAI #Innovation #EmergingTech #NGNN #AIHealthcare #Bioinformatics
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📌 Range Over Depth: A Reflection on the Role of the Data Generalist
🗂 Category: PRODUCTIVITY
🕒 Date: 2026-04-13 | ⏱️ Read time: 5 min read
What has changed in the past five years in the role and importance of generalists…
#DataScience #AI #Python
🗂 Category: PRODUCTIVITY
🕒 Date: 2026-04-13 | ⏱️ Read time: 5 min read
What has changed in the past five years in the role and importance of generalists…
#DataScience #AI #Python
❤1
📌 I Built a Tiny Computer Inside a Transformer
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-13 | ⏱️ Read time: 19 min read
By compiling a simple program directly into transformer weights.
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-13 | ⏱️ Read time: 19 min read
By compiling a simple program directly into transformer weights.
#DataScience #AI #Python
📌 How to Apply Claude Code to Non-technical Tasks
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-13 | ⏱️ Read time: 8 min read
Learn how to apply coding agents to all tasks on your computer
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-13 | ⏱️ Read time: 8 min read
Learn how to apply coding agents to all tasks on your computer
#DataScience #AI #Python
Synthetic Image Detection using Gradient Fields 💡🔍
A simple luminance-gradient PCA analysis reveals a consistent separation between real photographs and diffusion-generated images 📸🤖.
Real images produce coherent gradient fields tied to physical lighting and sensor characteristics ☀️📷, while diffusion samples show unstable high-frequency structures from the denoising process 🌀.
By converting RGB to luminance, computing spatial gradients, flattening them into a matrix, and evaluating the covariance through PCA, the difference becomes visible in a single projection 📊.
This provides a lightweight and interpretable way to assess image authenticity without relying on metadata or classifier models ✅🛡.
https://t.iss.one/DataScienceM💙
A simple luminance-gradient PCA analysis reveals a consistent separation between real photographs and diffusion-generated images 📸🤖.
Real images produce coherent gradient fields tied to physical lighting and sensor characteristics ☀️📷, while diffusion samples show unstable high-frequency structures from the denoising process 🌀.
By converting RGB to luminance, computing spatial gradients, flattening them into a matrix, and evaluating the covariance through PCA, the difference becomes visible in a single projection 📊.
This provides a lightweight and interpretable way to assess image authenticity without relying on metadata or classifier models ✅🛡.
https://t.iss.one/DataScienceM
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CVPR 2025 Best Paper: Visual Geometry Grounded Transformer (VGGT) ❤️ 🏆
VGGT shows that multi-view 3D reconstruction can be handled by a single feed-forward transformer, without relying on heavy test-time optimization. 🚀
Given one to hundreds of images, VGGT jointly predicts camera parameters 📷, depth maps, viewpoint-invariant point maps, and tracking features in a single forward pass. ⚡️
By combining DINO-based image tokenization, explicit camera tokens, and alternating frame-wise and global self-attention, the model learns multi-view geometry with minimal inductive bias. 🧠✨
https://t.iss.one/DataScienceM🩵
VGGT shows that multi-view 3D reconstruction can be handled by a single feed-forward transformer, without relying on heavy test-time optimization. 🚀
Given one to hundreds of images, VGGT jointly predicts camera parameters 📷, depth maps, viewpoint-invariant point maps, and tracking features in a single forward pass. ⚡️
By combining DINO-based image tokenization, explicit camera tokens, and alternating frame-wise and global self-attention, the model learns multi-view geometry with minimal inductive bias. 🧠
https://t.iss.one/DataScienceM
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Machine Learning
CVPR 2025 Best Paper: Visual Geometry Grounded Transformer (VGGT) ❤️ 🏆 VGGT shows that multi-view 3D reconstruction can be handled by a single feed-forward transformer, without relying on heavy test-time optimization. 🚀 Given one to hundreds of images, VGGT…
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📌 Data Modeling for Analytics Engineers: The Complete Primer
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-04-14 | ⏱️ Read time: 29 min read
The best data models make it hard to ask bad questions and easy to answer…
#DataScience #AI #Python
🗂 Category: DATA ENGINEERING
🕒 Date: 2026-04-14 | ⏱️ Read time: 29 min read
The best data models make it hard to ask bad questions and easy to answer…
#DataScience #AI #Python
📌 A Practical Guide to Choosing the Right Quantum SDK
🗂 Category: QUANTUM COMPUTING
🕒 Date: 2026-04-14 | ⏱️ Read time: 7 min read
What to use, when to use it, and what to ignore?
#DataScience #AI #Python
🗂 Category: QUANTUM COMPUTING
🕒 Date: 2026-04-14 | ⏱️ Read time: 7 min read
What to use, when to use it, and what to ignore?
#DataScience #AI #Python
📌 A Guide to Understanding GPUs and Maximizing GPU Utilization
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-14 | ⏱️ Read time: 18 min read
In an age of constrained compute, learn how to optimize GPU efficiency through understanding architecture,…
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-14 | ⏱️ Read time: 18 min read
In an age of constrained compute, learn how to optimize GPU efficiency through understanding architecture,…
#DataScience #AI #Python
📌 How To Produce Ultra-Compact Vector Graphic Plots With Orthogonal Distance Fitting
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-14 | ⏱️ Read time: 11 min read
Generate high-quality, minimal SVG plots by fitting Bézier curves with an ODF algorithm.
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-14 | ⏱️ Read time: 11 min read
Generate high-quality, minimal SVG plots by fitting Bézier curves with an ODF algorithm.
#DataScience #AI #Python
❤1
📌 Prefill Is Compute-Bound. Decode Is Memory-Bound. Why Your GPU Shouldn’t Do Both.
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-15 | ⏱️ Read time: 16 min read
Inside disaggregated LLM inference — the architecture shift behind 2-4x cost reduction that most ML…
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-15 | ⏱️ Read time: 16 min read
Inside disaggregated LLM inference — the architecture shift behind 2-4x cost reduction that most ML…
#DataScience #AI #Python
🔍 Exploring the Power of Minkowski Distance in Data Analysis 📊
Minkowski distance is a mathematical measure used to calculate the distance between two points in a multi-dimensional space. It's an extension of the more commonly known Euclidean distance, which we often encounter in our daily lives. However, Minkowski distance offers additional flexibility by allowing us to adjust its behavior based on a parameter called "p."
The formula for Minkowski distance is as follows:
D(x, y) = (∑|xi - yi|^p)^(1/p)
Here, xi and yi represent the coordinates of two points in the dataset. By varying the value of "p," we can adapt the calculation to suit different scenarios:
1️⃣ When p = 1, it becomes Manhattan distance (also known as City Block or Taxicab distance). It measures the sum of absolute differences between corresponding coordinates. This metric is useful when movement can only occur along straight lines.
2️⃣ When p = 2, it reduces to Euclidean distance. It calculates the straight-line distance between two points and is widely used across various fields.
3️⃣ When p → ∞, it represents Chebyshev distance. This measure considers only the maximum difference between coordinates and is particularly useful when movement can occur diagonally.
By leveraging Minkowski distance with different values of "p," we gain flexibility in analyzing data based on specific requirements and characteristics of our dataset.
Applications of Minkowski distance are vast and diverse:
✅ Clustering Analysis: It helps identify similar groups or clusters within datasets by measuring distances between points.
✅ Recommender Systems: By calculating distances between users or items based on their attributes, Minkowski distance can assist in generating personalized recommendations.
✅ Anomaly Detection: It aids in identifying outliers or anomalies by measuring the deviation of a data point from the rest.
✅ Image Processing: Minkowski distance plays a crucial role in image comparison, object recognition, and pattern matching tasks.
Understanding Minkowski distance opens up exciting possibilities for data scientists, analysts, and researchers to gain deeper insights into their datasets and make informed decisions. 📈
So, next time you encounter multi-dimensional data analysis challenges, remember to explore the power of Minkowski distance!🚀
https://t.iss.one/DataScienceM✈️
Minkowski distance is a mathematical measure used to calculate the distance between two points in a multi-dimensional space. It's an extension of the more commonly known Euclidean distance, which we often encounter in our daily lives. However, Minkowski distance offers additional flexibility by allowing us to adjust its behavior based on a parameter called "p."
The formula for Minkowski distance is as follows:
D(x, y) = (∑|xi - yi|^p)^(1/p)
Here, xi and yi represent the coordinates of two points in the dataset. By varying the value of "p," we can adapt the calculation to suit different scenarios:
1️⃣ When p = 1, it becomes Manhattan distance (also known as City Block or Taxicab distance). It measures the sum of absolute differences between corresponding coordinates. This metric is useful when movement can only occur along straight lines.
2️⃣ When p = 2, it reduces to Euclidean distance. It calculates the straight-line distance between two points and is widely used across various fields.
3️⃣ When p → ∞, it represents Chebyshev distance. This measure considers only the maximum difference between coordinates and is particularly useful when movement can occur diagonally.
By leveraging Minkowski distance with different values of "p," we gain flexibility in analyzing data based on specific requirements and characteristics of our dataset.
Applications of Minkowski distance are vast and diverse:
✅ Clustering Analysis: It helps identify similar groups or clusters within datasets by measuring distances between points.
✅ Recommender Systems: By calculating distances between users or items based on their attributes, Minkowski distance can assist in generating personalized recommendations.
✅ Anomaly Detection: It aids in identifying outliers or anomalies by measuring the deviation of a data point from the rest.
✅ Image Processing: Minkowski distance plays a crucial role in image comparison, object recognition, and pattern matching tasks.
Understanding Minkowski distance opens up exciting possibilities for data scientists, analysts, and researchers to gain deeper insights into their datasets and make informed decisions. 📈
So, next time you encounter multi-dimensional data analysis challenges, remember to explore the power of Minkowski distance!
https://t.iss.one/DataScienceM
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📌 5 Practical Tips for Transforming Your Batch Data Pipeline into Real-Time: Upcoming Webinar
🗂 Category: TDS WEBINARS
🕒 Date: 2026-04-15 | ⏱️ Read time: 5 min read
Bringing your batch pipeline to real-time requires careful consideration. This post brings you five practical…
#DataScience #AI #Python
🗂 Category: TDS WEBINARS
🕒 Date: 2026-04-15 | ⏱️ Read time: 5 min read
Bringing your batch pipeline to real-time requires careful consideration. This post brings you five practical…
#DataScience #AI #Python