π Ultimate Guide to Graph Neural Networks (GNNs): Part 1 β Foundations of Graph Theory & Why GNNs Revolutionize AI
Duration: ~45 minutes reading time | Comprehensive beginner-to-advanced introduction
Let's start: https://hackmd.io/@husseinsheikho/GNN-1
Duration: ~45 minutes reading time | Comprehensive beginner-to-advanced introduction
Let's start: https://hackmd.io/@husseinsheikho/GNN-1
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #NodeClassification #LinkPrediction #GraphRepresentation #AIforBeginners #AdvancedAIο»Ώ
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π Ultimate Guide to Graph Neural Networks (GNNs): Part 2 β The Message Passing Framework: Mathematical Heart of All GNNs
Duration: ~60 minutes reading time | Comprehensive deep dive into the core mechanism powering modern GNNs
Let's study: https://hackmd.io/@husseinsheikho/GNN-2
Duration: ~60 minutes reading time | Comprehensive deep dive into the core mechanism powering modern GNNs
Let's study: https://hackmd.io/@husseinsheikho/GNN-2
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #MessagePassing #GraphAlgorithms #NodeClassification #LinkPrediction #GraphRepresentation #AIforBeginners #AdvancedAI
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Duration: ~60 minutes reading time | Comprehensive deep dive into cutting-edge GNN architectures
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #GraphTransformers #TemporalGNNs #GeometricDeepLearning #AdvancedGNNs #AIforBeginners #AdvancedAI
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π Ultimate Guide to Graph Neural Networks (GNNs): Part 4 β GNN Training Dynamics, Optimization Challenges, and Scalability Solutions
Duration: ~45 minutes reading time | Comprehensive guide to training GNNs effectively at scale
Part 4-A: https://hackmd.io/@husseinsheikho/GNN4-A
Part4-B: https://hackmd.io/@husseinsheikho/GNN4-B
Duration: ~45 minutes reading time | Comprehensive guide to training GNNs effectively at scale
Part 4-A: https://hackmd.io/@husseinsheikho/GNN4-A
Part4-B: https://hackmd.io/@husseinsheikho/GNN4-B
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #GNNOptimization #ScalableGNNs #TrainingDynamics #AIforBeginners #AdvancedAI
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π Ultimate Guide to Graph Neural Networks (GNNs): Part 5 β GNN Applications Across Domains: Real-World Impact in 30 Minutes
Duration: ~30 minutes reading time | Practical guide to GNN applications with concrete ROI metrics
Link: https://hackmd.io/@husseinsheikho/GNN-5
Duration: ~30 minutes reading time | Practical guide to GNN applications with concrete ROI metrics
Link: https://hackmd.io/@husseinsheikho/GNN-5
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #RealWorldApplications #HealthcareAI #FinTech #DrugDiscovery #RecommendationSystems #ClimateAI
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π Ultimate Guide to Graph Neural Networks (GNNs): Part 6 β Advanced Frontiers, Ethics, and Future Directions
Duration: ~50 minutes reading time | Cutting-edge insights on where GNNs are headed
Let's read: https://hackmd.io/@husseinsheikho/GNN-6
Duration: ~50 minutes reading time | Cutting-edge insights on where GNNs are headed
Let's read: https://hackmd.io/@husseinsheikho/GNN-6
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #FutureOfGNNs #EmergingResearch #EthicalAI #GNNBestPractices #AdvancedAI #50MinuteRead
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π Ultimate Guide to Graph Neural Networks (GNNs): Part 7 β Advanced Implementation, Multimodal Integration, and Scientific Applications
Duration: ~60 minutes reading time | Deep dive into cutting-edge GNN implementations and applications
Read: https://hackmd.io/@husseinsheikho/GNN7
βοΈ Our Telegram channels: https://t.iss.one/addlist/0f6vfFbEMdAwODBk
Duration: ~60 minutes reading time | Deep dive into cutting-edge GNN implementations and applications
Read: https://hackmd.io/@husseinsheikho/GNN7
#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #AdvancedGNNs #MultimodalLearning #ScientificAI #GNNImplementation #60MinuteRead
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PyTorch Masterclass: Part 1 β Foundations of Deep Learning with PyTorch
Duration: ~120 minutes
Link: https://hackmd.io/@husseinsheikho/pytorch-1
https://t.iss.one/DataScienceMπ°
Duration: ~120 minutes
Link: https://hackmd.io/@husseinsheikho/pytorch-1
#PyTorch #DeepLearning #MachineLearning #AI #NeuralNetworks #DataScience #Python #Tensors #Autograd #Backpropagation #GradientDescent #AIForBeginners #PyTorchTutorial #MachineLearningEngineer
https://t.iss.one/DataScienceM
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β¨ NeRFs Explained: Goodbye Photogrammetry? β¨
π Table of Contents NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Stepsβ¦...
π·οΈ #3DComputerVision #3DReconstruction #DeepLearning #NeuralNetworks #Photogrammetry #Tutorial
π Table of Contents NeRFs Explained: Goodbye Photogrammetry? How Do NeRFs Work? Block #A: We Begin with a 5D Input Block #B: The Neural Network and Its Output Block #C: Volumetric Rendering The NeRF Problem and Evolutions Summary and Next Stepsβ¦...
π·οΈ #3DComputerVision #3DReconstruction #DeepLearning #NeuralNetworks #Photogrammetry #Tutorial
β¨ Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) β¨
π Table of Contents Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) Introduction to Advanced Adversarial Techniques in Machine Learning Harnessing NSL for Robust Model Training: Insights from Part 2 Deep Dive intoβ¦...
π·οΈ #AdversarialLearning #DeepLearning #ImageProcessing #Keras #MachineLearning #NeuralNetworks #NeuralStructuredLearning #TensorFlow #Tutorial
π Table of Contents Adversarial Learning with Keras and TensorFlow (Part 3): Exploring Adversarial Attacks Using Neural Structured Learning (NSL) Introduction to Advanced Adversarial Techniques in Machine Learning Harnessing NSL for Robust Model Training: Insights from Part 2 Deep Dive intoβ¦...
π·οΈ #AdversarialLearning #DeepLearning #ImageProcessing #Keras #MachineLearning #NeuralNetworks #NeuralStructuredLearning #TensorFlow #Tutorial
π€π§ The Little Book of Deep Learning β A Complete Summary and Chapter-Wise Overview
ποΈ 08 Oct 2025
π AI News & Trends
In the ever-evolving world of Artificial Intelligence, deep learning continues to be the driving force behind breakthroughs in computer vision, speech recognition and natural language processing. For those seeking a clear, structured and accessible guide to understanding how deep learning really works, βThe Little Book of Deep Learningβ by FranΓ§ois Fleuret is a gem. This ...
#DeepLearning #ArtificialIntelligence #MachineLearning #NeuralNetworks #AIGuides # FrancoisFleuret
ποΈ 08 Oct 2025
π AI News & Trends
In the ever-evolving world of Artificial Intelligence, deep learning continues to be the driving force behind breakthroughs in computer vision, speech recognition and natural language processing. For those seeking a clear, structured and accessible guide to understanding how deep learning really works, βThe Little Book of Deep Learningβ by FranΓ§ois Fleuret is a gem. This ...
#DeepLearning #ArtificialIntelligence #MachineLearning #NeuralNetworks #AIGuides # FrancoisFleuret
π I Measured Neural Network Training Every 5 Steps for 10,000 Iterations
π Category: MACHINE LEARNING
π Date: 2025-11-15 | β±οΈ Read time: 9 min read
A deep dive into the mechanics of neural network training. This detailed analysis meticulously measures key training metrics every 5 steps over 10,000 iterations, providing a high-resolution view of the learning process. The findings offer granular insights into model convergence and the subtle dynamics often missed by standard monitoring, making it a valuable read for ML practitioners and researchers seeking to better understand how models learn.
#NeuralNetworks #MachineLearning #DeepLearning #DataAnalysis #ModelTraining
π Category: MACHINE LEARNING
π Date: 2025-11-15 | β±οΈ Read time: 9 min read
A deep dive into the mechanics of neural network training. This detailed analysis meticulously measures key training metrics every 5 steps over 10,000 iterations, providing a high-resolution view of the learning process. The findings offer granular insights into model convergence and the subtle dynamics often missed by standard monitoring, making it a valuable read for ML practitioners and researchers seeking to better understand how models learn.
#NeuralNetworks #MachineLearning #DeepLearning #DataAnalysis #ModelTraining
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