Forwarded from Machine Learning with Python
The Big Book of Large Language Models by Damien Benveniste
✅ Chapters:
1⃣ Introduction
🔢 Language Models Before Transformers
🔢 Attention Is All You Need: The Original Transformer Architecture
🔢 A More Modern Approach To The Transformer Architecture
🔢 Multi-modal Large Language Models
🔢 Transformers Beyond Language Models
🔢 Non-Transformer Language Models
🔢 How LLMs Generate Text
🔢 From Words To Tokens
1⃣ 0⃣ Training LLMs to Follow Instructions
1⃣ 1⃣ Scaling Model Training
1⃣ 🔢 Fine-Tuning LLMs
1⃣ 🔢 Deploying LLMs
Read it: https://book.theaiedge.io/
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast
https://t.me/CodeProgrammer
Read it: https://book.theaiedge.io/
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast
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Forwarded from Machine Learning with Python
👨🏻💻 If you want to become a data science professional, follow this path! I've prepared a complete roadmap with the best free resources where you can learn the essential skills in this field.
#ArtificialIntelligence #AI #MachineLearning #LargeLanguageModels #LLMs #DeepLearning #NLP #NaturalLanguageProcessing #AIResearch #TechBooks #AIApplications #DataScience #FutureOfAI #AIEducation #LearnAI #TechInnovation #AIethics #GPT #BERT #T5 #AIBook #AIEnthusiast
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📌 Water Cooler Small Talk, Ep. 10: So, What About the AI Bubble?
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-11-27 | ⏱️ Read time: 10 min read
The tech world is buzzing with AI advancements, but is it a sustainable boom or a bubble on the verge of popping? This discussion explores the massive investments and lofty promises fueling the current AI hype, critically examining whether we're being sold an impossibly expensive and unrealistic future.
#AIBubble #ArtificialIntelligence #TechTrends #FutureOfAI
🗂 Category: ARTIFICIAL INTELLIGENCE
🕒 Date: 2025-11-27 | ⏱️ Read time: 10 min read
The tech world is buzzing with AI advancements, but is it a sustainable boom or a bubble on the verge of popping? This discussion explores the massive investments and lofty promises fueling the current AI hype, critically examining whether we're being sold an impossibly expensive and unrealistic future.
#AIBubble #ArtificialIntelligence #TechTrends #FutureOfAI
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Forwarded from AI & ML Papers
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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