Data Science Machine Learning Data Analysis
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This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

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🌟 Vision Transformer (ViT) Tutorial – Part 3: Pretraining, Transfer Learning & Real-World Applications

Let's start: https://hackmd.io/@husseinsheikho/vit-3

#VisionTransformer #TransferLearning #HuggingFace #ImageNet #FineTuning #AI #DeepLearning #ComputerVision #Transformers #ModelZoo


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🌟 Vision Transformer (ViT) Tutorial – Part 4: Beyond Classification – DETR, Segmentation & Video Transformers

Let's start learn: https://hackmd.io/@husseinsheikho/vit-4

#VisionTransformer #DETR #Segmenter #VideoTransformer #MAE #SelfSupervised #Multimodal #AI #DeepLearning #ComputerVision

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🌟 Vision Transformer (ViT) Tutorial – Part 5: Efficient Vision Transformers – MobileViT, TinyViT & Edge Deployment

Read lesson: https://hackmd.io/@husseinsheikho/vit-5

#MobileViT #TinyViT #EfficientViT #EdgeAI #ModelOptimization #ONNX #TensorRT #TorchServe #DeepLearning #ComputerVision #Transformers

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🌟 Vision Transformer (ViT) Tutorial – Part 6: Vision Transformers in Production – MLOps, Monitoring & CI/CD

Learn more: https://hackmd.io/@husseinsheikho/vit-6

#MLOps #ModelMonitoring #CIforML #MLflow #WandB #Kubeflow #ProductionAI #DeepLearning #ComputerVision #Transformers #AIOps

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🌟 Vision Transformer (ViT) Tutorial – Part 7: The Future of Vision Transformers – Multimodal, 3D, and Beyond

Learn: https://hackmd.io/@husseinsheikho/vit-7

#FutureOfViT #MultimodalAI #3DViT #TimeSformer #PaLME #MedicalAI #EmbodiedAI #RetNet #Mamba #NextGenAI #DeepLearning #ComputerVision #Transformers

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🔥 Master Vision Transformers with 65+ MCQs! 🔥

Are you preparing for AI interviews or want to test your knowledge in Vision Transformers (ViT)?

🧠 Dive into 65+ curated Multiple Choice Questions covering the fundamentals, architecture, training, and applications of ViT — all with answers!

🌐 Explore Now: https://hackmd.io/@husseinsheikho/vit-mcq

🔹 Table of Contents
Basic Concepts (Q1–Q15)
Architecture & Components (Q16–Q30)
Attention & Transformers (Q31–Q45)
Training & Optimization (Q46–Q55)
Advanced & Real-World Applications (Q56–Q65)
Answer Key & Explanations

#VisionTransformer #ViT #DeepLearning #ComputerVision #Transformers #AI #MachineLearning #MCQ #InterviewPrep


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🚀 Become an Agentic AI Builder — Free 12‑Week Certification by Ready Tensor

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The course focuses on developing autonomous AI agents that can plan, reason, use memory, and act safely in complex environments. Certification is earned not by watching lectures, but by building — each project is reviewed against rigorous standards.

You can start anytime, and new cohorts begin monthly. Ideal for developers and engineers ready to go beyond chat prompts and start building true agentic systems.

👉 Apply now: https://www.readytensor.ai/agentic-ai-cert/
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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 1 — 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

#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #NodeClassification #LinkPrediction #GraphRepresentation #AIforBeginners #AdvancedAI

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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 2 — 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

#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #MessagePassing #GraphAlgorithms #NodeClassification #LinkPrediction #GraphRepresentation #AIforBeginners #AdvancedAI

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📕 Ultimate Guide to Graph Neural Networks (GNNs): Part 3 — Advanced GNN Architectures: Transformers, Temporal Networks & Geometric Deep Learning

Duration: ~60 minutes reading time | Comprehensive deep dive into cutting-edge GNN architectures

🆘 Read: https://hackmd.io/@husseinsheikho/GNN-3

#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #GraphTransformers #TemporalGNNs #GeometricDeepLearning #AdvancedGNNs #AIforBeginners #AdvancedAI


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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 4 — 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

#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #PyTorchGeometric #GNNOptimization #ScalableGNNs #TrainingDynamics #AIforBeginners #AdvancedAI


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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 5 — 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

#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #RealWorldApplications #HealthcareAI #FinTech #DrugDiscovery #RecommendationSystems #ClimateAI

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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 6 — 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

#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #FutureOfGNNs #EmergingResearch #EthicalAI #GNNBestPractices #AdvancedAI #50MinuteRead

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📘 Ultimate Guide to Graph Neural Networks (GNNs): Part 7 — 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

#GraphNeuralNetworks #GNN #MachineLearning #DeepLearning #AI #NeuralNetworks #DataScience #GraphTheory #ArtificialIntelligence #AdvancedGNNs #MultimodalLearning #ScientificAI #GNNImplementation #60MinuteRead

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🥇 This repo is like gold for every data scientist!

Just open your browser; a ton of interactive exercises and real experiences await you. Any question about statistics, probability, Python, or machine learning, you'll get the answer right there! With code, charts, even animations. This way, you don't waste time, and what you learn really sticks in your mind!

⬅️ Data science statistics and probability topics
⬅️ Clustering
⬅️ Principal Component Analysis (PCA)
⬅️ Bagging and Boosting techniques
⬅️ Linear regression
⬅️ Neural networks and more...


📂 Int Data Science Python Dash
🐱 GitHub-Repos

👉 @codeprogrammer
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