Metadata: How data about your data is optimal for AI
Discover how metadata enhances AI performance by providing crucial context for models. Learn key benefits, implementation strategies, and real-world examples for smarter AI systems.
This rubric covers articles that focus primarily on Python code, the Python language, or libraries that are primarily within the Python sphere, such as Pandas or Numpy.
Discover how metadata enhances AI performance by providing crucial context for models. Learn key benefits, implementation strategies, and real-world examples for smarter AI systems.
With explainable AI, intuitive parameters easy to fine-tune, versatile, robust, fast to train, without any library other than Numpy. In short, you have full control… Read More »How to Build and Optimize High-Performance Deep Neural Networks from Scratch
This book opens up new research areas in theoretical and computational number theory, numerical approximation, dynamical systems, quantum dynamics, and the physics of numbers. With… Read More »New Book: 0 and 1 – From Elemental Math to Quantum AI
Interview / Jerry Yurchisin, Sr. Data Scientist @ Gurobi As a lifelong systems thinker and technology advocate, I’ve long been fascinated by the intersection of… Read More »Mathematical optimization for AI
Welcome to another comparison article where you will understand the features, intricacies, pros, and cons of two different stacks of the information technology industry. Today’s… Read More »Python vs R for data science: Which should you choose?
AI in Agriculture Precision Farming AI-Powered Agriculture Climate-Resilient Crops
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Predictive Analytics in Farming AI for Weather Prediction in Agriculture
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AI-Powered Smart Irrigation Agricultural Innovation with AI
Tools such as OpenAI can on occasion give the impression that they are able to prove theorems and even generalize them. Whether this is a… Read More »Using AI to Solve the Deepest Math Conjectures
In this article, I share my latest Gen AI and LLM advances, featuring innovative approaches radically different from both standard AI and classical ML/NLP. The… Read More »LLM 2.0, RAG & Non-Standard Gen AI on GitHub
Introduction Sentiment analysis, also known as opinion mining, is a powerful concept in the Natural Language Processing (NLP) technique that interprets and classifies emotions expressed… Read More »Sentiment analysis at scale: Applying NLP to multi-lingual and domain-specific texts
Feature Selection is a crucial process in feature engineering as part of the Machine Learning life cycle. It focuses on identifying the most impactful features in… Read More »Techniques for automated feature selection: Filter methods and implementation using Python libraries