📌 Detecting Translation Hallucinations with Attention Misalignment
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-08 | ⏱️ Read time: 15 min read
A low-budget way to get token-level uncertainty estimation for neural machine translations
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-08 | ⏱️ Read time: 15 min read
A low-budget way to get token-level uncertainty estimation for neural machine translations
#DataScience #AI #Python
📌 How to Use Claude Code to Build a Minimum Viable Product
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-08 | ⏱️ Read time: 8 min read
Learn how to effectively present product ideas by building MVPs with coding agents
#DataScience #AI #Python
🗂 Category: AGENTIC AI
🕒 Date: 2026-04-08 | ⏱️ Read time: 8 min read
Learn how to effectively present product ideas by building MVPs with coding agents
#DataScience #AI #Python
Forwarded from Machine Learning with Python
✔️ 10 Books to Understand How Large Language Models Function (2026)
1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.
2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.
3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.
4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.
5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.
6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.
7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.
8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.
9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.
10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. 📚🤖
1. Deep Learning
https://deeplearningbook.org
The definitive reference for neural networks, covering backpropagation, architectures, and foundational concepts.
2. Artificial Intelligence: A Modern Approach
https://aima.cs.berkeley.edu
A fundamental perspective on artificial intelligence as a comprehensive system.
3. Speech and Language Processing
https://web.stanford.edu/~jurafsky/slp3/
An in-depth examination of natural language processing, transformers, and linguistics.
4. Machine Learning: A Probabilistic Perspective
https://probml.github.io/pml-book/
An exploration of probabilities, statistics, and the theoretical foundations of machine learning.
5. Understanding Deep Learning
https://udlbook.github.io/udlbook/
A contemporary explanation of deep learning principles with strong intuitive insights.
6. Designing Machine Learning Systems
https://oreilly.com/library/view/designing-machine-learning/9781098107956/
Strategies for deploying models into production environments.
7. Generative Deep Learning
https://github.com/3p5ilon/ML-books/blob/main/generative-deep-learning-teaching-machines-to-paint-write-compose-and-play.pdf
Practical applications of generative models and transformer architectures.
8. Natural Language Processing with Transformers
https://dokumen.pub/natural-language-processing-with-transformers-revised-edition-1098136799-9781098136796-9781098103248.html
Methodologies for constructing natural language processing systems based on transformers.
9. Machine Learning Engineering
https://mlebook.com
Principles of machine learning engineering and operational deployment.
10. The Hundred-Page Machine Learning Book
https://themlbook.com
A highly concentrated foundational overview without extraneous detail. 📚🤖
❤1
📌 Grounding Your LLM: A Practical Guide to RAG for Enterprise Knowledge Bases
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-08 | ⏱️ Read time: 17 min read
A clear mental model and a practical foundation you can build on
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-08 | ⏱️ Read time: 17 min read
A clear mental model and a practical foundation you can build on
#DataScience #AI #Python
How a University Student Built a Game Changing Bot for Polymarket – And You Can Use It Too
A computer science student built a bot that snipes trades before the market reacts! Meet Peter, who automated crypto trading by tracking blockchain data delays. He created the Oracle Lag Sniper to get in on Polymarket trades faster than anyone else.
⚡ Why it works:
• Super Fast Execution: Snipes trades before the market catches up
• Polymarket-Optimized: Built for speed & accuracy
• Open Source & Free: Tweak it as you wish
• Easy Setup: No tech skills required!
Start using the Oracle Lag Sniper today. Head to GitHub, set it up, and make smarter, quicker trades.
Sponsored by Polymarket Analytics
A computer science student built a bot that snipes trades before the market reacts! Meet Peter, who automated crypto trading by tracking blockchain data delays. He created the Oracle Lag Sniper to get in on Polymarket trades faster than anyone else.
⚡ Why it works:
• Super Fast Execution: Snipes trades before the market catches up
• Polymarket-Optimized: Built for speed & accuracy
• Open Source & Free: Tweak it as you wish
• Easy Setup: No tech skills required!
Start using the Oracle Lag Sniper today. Head to GitHub, set it up, and make smarter, quicker trades.
Sponsored by Polymarket Analytics
❤2🔥2
📌 A Visual Explanation of Linear Regression
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-09 | ⏱️ Read time: 107 min read
A long-form article featuring over 100 visualizations, covering a range of topics from how to…
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-09 | ⏱️ Read time: 107 min read
A long-form article featuring over 100 visualizations, covering a range of topics from how to…
#DataScience #AI #Python
❤1
📌 How Visual-Language-Action (VLA) Models Work
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-09 | ⏱️ Read time: 18 min read
The mathematical foundations of Vision-Language-Action (VLA) models for humanoid robots and more
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-09 | ⏱️ Read time: 18 min read
The mathematical foundations of Vision-Language-Action (VLA) models for humanoid robots and more
#DataScience #AI #Python
📌 A Survival Analysis Guide with Python: Using Time-To-Event Models to Forecast Customer Lifetime
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-09 | ⏱️ Read time: 13 min read
Understand survival analysis by modeling customer retention through Kaplan-Meier curves and Cox Proportional Hazard regressions.
#DataScience #AI #Python
🗂 Category: DATA SCIENCE
🕒 Date: 2026-04-09 | ⏱️ Read time: 13 min read
Understand survival analysis by modeling customer retention through Kaplan-Meier curves and Cox Proportional Hazard regressions.
#DataScience #AI #Python
📌 The Future of AI for Sales Is Diverse and Distributed
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-09 | ⏱️ Read time: 11 min read
True creativity and innovation will come from human-agent collaboration. One human, millions of agents.
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-09 | ⏱️ Read time: 11 min read
True creativity and innovation will come from human-agent collaboration. One human, millions of agents.
#DataScience #AI #Python
👍1
📌 Why MLOps Retraining Schedules Fail — Models Don’t Forget, They Get Shocked
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-10 | ⏱️ Read time: 17 min read
We fitted the Ebbinghaus forgetting curve to 555,000 real fraud transactions and got R² =…
#DataScience #AI #Python
🗂 Category: MACHINE LEARNING
🕒 Date: 2026-04-10 | ⏱️ Read time: 17 min read
We fitted the Ebbinghaus forgetting curve to 555,000 real fraud transactions and got R² =…
#DataScience #AI #Python
👍1
📌 A Guide to Voice Cloning on Voxtral with a Missing Encoder
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-10 | ⏱️ Read time: 13 min read
Can we reconstruct audio codes if we have audio for the Voxtral text-to-speech model?
#DataScience #AI #Python
🗂 Category: LARGE LANGUAGE MODELS
🕒 Date: 2026-04-10 | ⏱️ Read time: 13 min read
Can we reconstruct audio codes if we have audio for the Voxtral text-to-speech model?
#DataScience #AI #Python
📌 How Does AI Learn to See in 3D and Understand Space?
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-10 | ⏱️ Read time: 19 min read
How depth estimation, foundation segmentation, and geometric fusion are converging into spatial intelligence
#DataScience #AI #Python
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2026-04-10 | ⏱️ Read time: 19 min read
How depth estimation, foundation segmentation, and geometric fusion are converging into spatial intelligence
#DataScience #AI #Python
❤3👎1🤩1
Forwarded from Machine Learning with Python
📝 12 Essential Articles for Data Scientists
🏷 Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.
🏷 Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.
🏷 Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.
🏷 Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.
🏷 Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.
🏷 Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.
🏷 Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.
🏷 Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.
🏷 Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.
🏷 Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.
🏷 Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.
🏷 Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.
https://t.iss.one/CodeProgrammer🌟
🏷 Article: Seq2Seq Learning with NN
https://arxiv.org/pdf/1409.3215
An introduction to Seq2Seq models, which serve as the foundation for machine translation utilizing deep learning.
🏷 Article: GANs
https://arxiv.org/pdf/1406.2661
An introduction to Generative Adversarial Networks (GANs) and the concept of generating synthetic data. This forms the basis for creating images and videos with artificial intelligence.
🏷 Article: Attention is All You Need
https://arxiv.org/pdf/1706.03762
This paper was revolutionary in natural language processing. It introduced the Transformer architecture, which underlies GPT, BERT, and contemporary intelligent language models.
🏷 Article: Deep Residual Learning
https://arxiv.org/pdf/1512.03385
This work introduced the ResNet model, enabling neural networks to achieve greater depth and accuracy without compromising the learning process.
🏷 Article: Batch Normalization
https://arxiv.org/pdf/1502.03167
This paper introduced a technique that facilitates faster and more stable training of neural networks.
🏷 Article: Dropout
https://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf
A straightforward method designed to prevent overfitting in neural networks.
🏷 Article: ImageNet Classification with DCNN
https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
The first successful application of a deep neural network for image recognition.
🏷 Article: Support-Vector Machines
https://link.springer.com/content/pdf/10.1007/BF00994018.pdf
This seminal work introduced the Support Vector Machine (SVM) algorithm, a widely utilized method for data classification.
🏷 Article: A Few Useful Things to Know About ML
https://homes.cs.washington.edu/~pedro/papers/cacm12.pdf
A comprehensive collection of practical and empirical insights regarding machine learning.
🏷 Article: Gradient Boosting Machine
https://www.cse.iitb.ac.in/~soumen/readings/papers/Friedman1999GreedyFuncApprox.pdf
This paper introduced the "Gradient Boosting" method, which serves as the foundation for many modern machine learning models, including XGBoost and LightGBM.
🏷 Article: Latent Dirichlet Allocation
https://jmlr.org/papers/volume3/blei03a/blei03a.pdf
This work introduced a model for text analysis capable of identifying the topics discussed within an article.
🏷 Article: Random Forests
https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
This paper introduced the "Random Forest" algorithm, a powerful machine learning method that aggregates multiple models to achieve enhanced accuracy.
https://t.iss.one/CodeProgrammer
Please open Telegram to view this post
VIEW IN TELEGRAM
❤1
📌 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
❤1