π Master Data Science & Programming!
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π° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
π Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
π§ Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
πΎ Kaggle Data Hub
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
π§βπ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
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π ML Research Hub
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
π Python Arab| Ψ¨Ψ§ΩΨ«ΩΩ ΨΉΨ±Ψ¨Ω
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
π Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
πΊ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
π§ Learn Python Hub
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
βοΈ Research Papers
Professional Academic Writing & Simulation Services
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Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
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https://t.me/DataScienceY
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β€1
β¨A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers
π Summary:
CoLog is a log anomaly detection framework using collaborative transformers and a modality adaptation layer to accurately detect both point and collective anomalies across diverse log data. It achieves high precision and recall over 99% on benchmark datasets, outperforming existing methods.
πΉ Publication Date: Published on Dec 29, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23380
β’ PDF: https://arxiv.org/pdf/2512.23380
β’ Project Page: https://www.alarmif.com
β’ Github: https://github.com/NasirzadehMoh/CoLog
==================================
For more data science resources:
β https://t.me/DataScienceT
#AnomalyDetection #LogAnalysis #Transformers #MachineLearning #Cybersecurity
π Summary:
CoLog is a log anomaly detection framework using collaborative transformers and a modality adaptation layer to accurately detect both point and collective anomalies across diverse log data. It achieves high precision and recall over 99% on benchmark datasets, outperforming existing methods.
πΉ Publication Date: Published on Dec 29, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23380
β’ PDF: https://arxiv.org/pdf/2512.23380
β’ Project Page: https://www.alarmif.com
β’ Github: https://github.com/NasirzadehMoh/CoLog
==================================
For more data science resources:
β https://t.me/DataScienceT
#AnomalyDetection #LogAnalysis #Transformers #MachineLearning #Cybersecurity
β€2
β¨Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation
π Summary:
DiffusionGS is a novel single-stage 3D diffusion model that directly generates 3D Gaussian point clouds from a single image. It ensures strong view consistency from any prompt view. This method achieves superior quality and is over 5x faster than state-of-the-art techniques.
πΉ Publication Date: Published on Nov 21, 2024
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2411.14384
β’ PDF: https://arxiv.org/pdf/2411.14384
β’ Project Page: https://caiyuanhao1998.github.io/project/DiffusionGS/
β’ Github: https://github.com/caiyuanhao1998/Open-DiffusionGS
πΉ Models citing this paper:
β’ https://huggingface.co/CaiYuanhao/DiffusionGS
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/CaiYuanhao/DiffusionGS
==================================
For more data science resources:
β https://t.me/DataScienceT
#3DGeneration #DiffusionModels #GaussianSplatting #ComputerVision #AIResearch
π Summary:
DiffusionGS is a novel single-stage 3D diffusion model that directly generates 3D Gaussian point clouds from a single image. It ensures strong view consistency from any prompt view. This method achieves superior quality and is over 5x faster than state-of-the-art techniques.
πΉ Publication Date: Published on Nov 21, 2024
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2411.14384
β’ PDF: https://arxiv.org/pdf/2411.14384
β’ Project Page: https://caiyuanhao1998.github.io/project/DiffusionGS/
β’ Github: https://github.com/caiyuanhao1998/Open-DiffusionGS
πΉ Models citing this paper:
β’ https://huggingface.co/CaiYuanhao/DiffusionGS
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/CaiYuanhao/DiffusionGS
==================================
For more data science resources:
β https://t.me/DataScienceT
#3DGeneration #DiffusionModels #GaussianSplatting #ComputerVision #AIResearch
arXiv.org
Baking Gaussian Splatting into Diffusion Denoiser for Fast and...
Existing feedforward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt view direction...
β¨LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference
π Summary:
LMCACHE is an efficient open-source solution for offloading and transferring LLM KV caches from GPU memory. It enables cache reuse across different queries and inference engines, addressing the problem of growing cache sizes. This improves throughput up to 15 times.
πΉ Publication Date: Published on Oct 8, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.09665
β’ PDF: https://arxiv.org/pdf/2510.09665
β’ Github: https://github.com/LMCache/LMCache
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #KVCache #GPU #AIInference #PerformanceOptimization
π Summary:
LMCACHE is an efficient open-source solution for offloading and transferring LLM KV caches from GPU memory. It enables cache reuse across different queries and inference engines, addressing the problem of growing cache sizes. This improves throughput up to 15 times.
πΉ Publication Date: Published on Oct 8, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2510.09665
β’ PDF: https://arxiv.org/pdf/2510.09665
β’ Github: https://github.com/LMCache/LMCache
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #KVCache #GPU #AIInference #PerformanceOptimization
β¨Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space
π Summary:
DLCM shifts computation from individual tokens to a compressed concept space, enabling more efficient reasoning. This hierarchical approach learns semantic boundaries end-to-end and improves performance on benchmarks by reallocating compute.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24617
β’ PDF: https://arxiv.org/pdf/2512.24617
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #MachineLearning #LargeModels #RepresentationLearning #EfficientAI
π Summary:
DLCM shifts computation from individual tokens to a compressed concept space, enabling more efficient reasoning. This hierarchical approach learns semantic boundaries end-to-end and improves performance on benchmarks by reallocating compute.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24617
β’ PDF: https://arxiv.org/pdf/2512.24617
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #MachineLearning #LargeModels #RepresentationLearning #EfficientAI
β¨On the Role of Discreteness in Diffusion LLMs
π Summary:
This paper examines diffusion language models, highlighting five properties separating diffusion mechanics from language requirements. Existing approaches face structural trade-offs. Key issues identified are uniform corruption and token-wise marginal training, urging development of diffusion pro...
πΉ Publication Date: Published on Dec 27, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22630
β’ PDF: https://arxiv.org/pdf/2512.22630
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
This paper examines diffusion language models, highlighting five properties separating diffusion mechanics from language requirements. Existing approaches face structural trade-offs. Key issues identified are uniform corruption and token-wise marginal training, urging development of diffusion pro...
πΉ Publication Date: Published on Dec 27, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.22630
β’ PDF: https://arxiv.org/pdf/2512.22630
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β¨DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
π Summary:
DiffThinker introduces a generative multimodal reasoning framework using diffusion models. It reframes vision-centric tasks as image-to-image generation for superior logical consistency and spatial precision. DiffThinker significantly outperforms existing MLLMs across various domains, showcasing ...
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24165
β’ PDF: https://arxiv.org/pdf/2512.24165
β’ Project Page: https://diffthinker-project.github.io/
β’ Github: https://github.com/lcqysl/DiffThinker
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
π Summary:
DiffThinker introduces a generative multimodal reasoning framework using diffusion models. It reframes vision-centric tasks as image-to-image generation for superior logical consistency and spatial precision. DiffThinker significantly outperforms existing MLLMs across various domains, showcasing ...
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24165
β’ PDF: https://arxiv.org/pdf/2512.24165
β’ Project Page: https://diffthinker-project.github.io/
β’ Github: https://github.com/lcqysl/DiffThinker
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #DataScience #MachineLearning #HuggingFace #Research
β€1
β¨AI-native Memory 2.0: Second Me
π Summary:
SECOND ME is an AI-native memory management system utilizing LLMs to reduce redundant user input. It intelligently retains and uses user knowledge for context-aware responses and prefilling, streamlining interactions.
πΉ Publication Date: Published on Mar 11, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2503.08102
β’ PDF: https://arxiv.org/pdf/2503.08102
β’ Github: https://github.com/Mindverse/Second-Me
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #LLM #MemoryManagement #HCI #NLP
π Summary:
SECOND ME is an AI-native memory management system utilizing LLMs to reduce redundant user input. It intelligently retains and uses user knowledge for context-aware responses and prefilling, streamlining interactions.
πΉ Publication Date: Published on Mar 11, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2503.08102
β’ PDF: https://arxiv.org/pdf/2503.08102
β’ Github: https://github.com/Mindverse/Second-Me
==================================
For more data science resources:
β https://t.me/DataScienceT
#AI #LLM #MemoryManagement #HCI #NLP
β€2
β¨Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling
π Summary:
Existing multi-step RAG memory limits reasoning by storing isolated facts and neglecting high-order correlations. HGMem proposes a hypergraph-based memory that dynamically forms higher-order interactions, creating an integrated knowledge structure. This approach significantly improves multi-step ...
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23959
β’ PDF: https://arxiv.org/pdf/2512.23959
β’ Github: https://github.com/Encyclomen/HGMem
==================================
For more data science resources:
β https://t.me/DataScienceT
#RAG #Hypergraphs #NLP #AI #LLM
π Summary:
Existing multi-step RAG memory limits reasoning by storing isolated facts and neglecting high-order correlations. HGMem proposes a hypergraph-based memory that dynamically forms higher-order interactions, creating an integrated knowledge structure. This approach significantly improves multi-step ...
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.23959
β’ PDF: https://arxiv.org/pdf/2512.23959
β’ Github: https://github.com/Encyclomen/HGMem
==================================
For more data science resources:
β https://t.me/DataScienceT
#RAG #Hypergraphs #NLP #AI #LLM
β¨Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow
π Summary:
Dream2Flow bridges video generation and robotic control using 3D object flow. It reconstructs 3D object motions from generated videos, enabling zero-shot manipulation of diverse objects through trajectory tracking without task-specific demonstrations.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24766
β’ PDF: https://arxiv.org/pdf/2512.24766
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #Robotics #3DVision #AI #ZeroShotLearning
π Summary:
Dream2Flow bridges video generation and robotic control using 3D object flow. It reconstructs 3D object motions from generated videos, enabling zero-shot manipulation of diverse objects through trajectory tracking without task-specific demonstrations.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24766
β’ PDF: https://arxiv.org/pdf/2512.24766
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #Robotics #3DVision #AI #ZeroShotLearning
β¨FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation
π Summary:
FlowBlending optimizes video generation by adapting model capacity to each stage. It uses large models for critical early and late timesteps, and small models for intermediate ones. This achieves faster inference and fewer FLOPs with no loss in large model fidelity.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24724
β’ PDF: https://arxiv.org/pdf/2512.24724
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #DeepLearning #AIResearch #ModelOptimization
π Summary:
FlowBlending optimizes video generation by adapting model capacity to each stage. It uses large models for critical early and late timesteps, and small models for intermediate ones. This achieves faster inference and fewer FLOPs with no loss in large model fidelity.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24724
β’ PDF: https://arxiv.org/pdf/2512.24724
==================================
For more data science resources:
β https://t.me/DataScienceT
#VideoGeneration #GenerativeAI #DeepLearning #AIResearch #ModelOptimization
π Master Data Science & Programming!
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
π° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
π Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
π§ Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
πΎ Kaggle Data Hub
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
π§βπ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
π ML Research Hub
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
π Python Arab| Ψ¨Ψ§ΩΨ«ΩΩ ΨΉΨ±Ψ¨Ω
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
π Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
πΊ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
π§ Learn Python Hub
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
βοΈ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
ββββββββββββββββββ
Admin: @HusseinSheikho
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
ββββββββββββββββββ
Admin: @HusseinSheikho
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β€2
β¨TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
π Summary:
TESO is a new metaheuristic framework for simulation optimization that tackles noisy, complex problems. It integrates Tabu List and Elite Memory strategies to dynamically balance exploration and exploitation, demonstrating improved performance.
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24007
β’ PDF: https://arxiv.org/pdf/2512.24007
β’ Github: https://github.com/bulentsoykan/TESO
==================================
For more data science resources:
β https://t.me/DataScienceT
#SimulationOptimization #Metaheuristics #Optimization #BlackBoxOptimization #TabuSearch
π Summary:
TESO is a new metaheuristic framework for simulation optimization that tackles noisy, complex problems. It integrates Tabu List and Elite Memory strategies to dynamically balance exploration and exploitation, demonstrating improved performance.
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24007
β’ PDF: https://arxiv.org/pdf/2512.24007
β’ Github: https://github.com/bulentsoykan/TESO
==================================
For more data science resources:
β https://t.me/DataScienceT
#SimulationOptimization #Metaheuristics #Optimization #BlackBoxOptimization #TabuSearch
β€1
This channels is for Programmers, Coders, Software Engineers.
0οΈβ£ Python
1οΈβ£ Data Science
2οΈβ£ Machine Learning
3οΈβ£ Data Visualization
4οΈβ£ Artificial Intelligence
5οΈβ£ Data Analysis
6οΈβ£ Statistics
7οΈβ£ Deep Learning
8οΈβ£ programming Languages
β
https://t.me/addlist/8_rRW2scgfRhOTc0
β
https://t.me/Codeprogrammer
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β€1
π Master Data Science & Programming!
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
π° Machine Learning with Python
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
π Machine Learning
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
π§ Code With Python
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
π― PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
πΎ Kaggle Data Hub
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
π§βπ Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
π ML Research Hub
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
π¬ Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
π Python Arab| Ψ¨Ψ§ΩΨ«ΩΩ ΨΉΨ±Ψ¨Ω
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
π Data Science Jupyter Notebooks
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
πΊ Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
π Data Analytics
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
π§ Learn Python Hub
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
βοΈ Research Papers
Professional Academic Writing & Simulation Services
https://t.me/DataScienceY
ββββββββββββββββββ
Admin: @HusseinSheikho
Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
https://t.me/CodeProgrammer
Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.me/DataScienceQ
Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.me/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
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β¨Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting
π Summary:
Dolphin is a novel multimodal model for document image parsing. It uses an analyze-then-parse approach with heterogeneous anchor prompting, achieving state-of-the-art performance and superior efficiency.
πΉ Publication Date: Published on May 20, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2505.14059
β’ PDF: https://arxiv.org/pdf/2505.14059
β’ Github: https://github.com/bytedance/dolphin
==================================
For more data science resources:
β https://t.me/DataScienceT
#DocumentParsing #MultimodalAI #DeepLearning #ComputerVision #AI
π Summary:
Dolphin is a novel multimodal model for document image parsing. It uses an analyze-then-parse approach with heterogeneous anchor prompting, achieving state-of-the-art performance and superior efficiency.
πΉ Publication Date: Published on May 20, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2505.14059
β’ PDF: https://arxiv.org/pdf/2505.14059
β’ Github: https://github.com/bytedance/dolphin
==================================
For more data science resources:
β https://t.me/DataScienceT
#DocumentParsing #MultimodalAI #DeepLearning #ComputerVision #AI
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Unlock your potential with this curated list of Telegram channels. Whether you need books, datasets, interview prep, or project ideas, we have the perfect resource for you. Join the community today!
Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers.
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Machine learning insights, practical tutorials, and clear explanations for beginners and aspiring data scientists. Follow the channel for models, algorithms, coding guides, and real-world ML applications.
https://t.me/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA β perfect for learning, coding, and mastering key programming skills.
https://t.me/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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Your go-to hub for Kaggle datasets β explore, analyze, and leverage data for Machine Learning and Data Science projects.
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The first channel in Telegram that offers free Udemy coupons
https://t.me/DataScienceC
Advancing research in Machine Learning β practical insights, tools, and techniques for researchers.
https://t.me/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.me/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.me/PythonArab
Explore the world of Data Science through Jupyter Notebooksβinsights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
https://t.me/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://t.me/DataScienceV
Dive into the world of Data Analytics β uncover insights, explore trends, and master data-driven decision making.
https://t.me/DataAnalyticsX
Master Python with step-by-step courses β from basics to advanced projects and practical applications.
https://t.me/Python53
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ββββββββββββββββββ
Admin: @HusseinSheikho
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β¨Youtu-Agent: Scaling Agent Productivity with Automated Generation and Hybrid Policy Optimization
π Summary:
Youtu-Agent scales LLM agent productivity, automating generation and enabling continuous evolution. Its hybrid optimization, using in-context learning and scalable reinforcement learning, yields top performance and boosted capabilities.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24615
β’ PDF: https://arxiv.org/pdf/2512.24615
β’ Project Page: https://tencentcloudadp.github.io/youtu-agent/
β’ Github: https://github.com/TencentCloudADP/youtu-tip
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #AIAgents #ReinforcementLearning #MachineLearning #AI
π Summary:
Youtu-Agent scales LLM agent productivity, automating generation and enabling continuous evolution. Its hybrid optimization, using in-context learning and scalable reinforcement learning, yields top performance and boosted capabilities.
πΉ Publication Date: Published on Dec 31, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24615
β’ PDF: https://arxiv.org/pdf/2512.24615
β’ Project Page: https://tencentcloudadp.github.io/youtu-agent/
β’ Github: https://github.com/TencentCloudADP/youtu-tip
==================================
For more data science resources:
β https://t.me/DataScienceT
#LLM #AIAgents #ReinforcementLearning #MachineLearning #AI
β¨SenseNova-MARS: Empowering Multimodal Agentic Reasoning and Search via Reinforcement Learning
π Summary:
SenseNova-MARS empowers Vision-Language Models with interleaved visual reasoning and dynamic tool use like search and cropping via reinforcement learning. It achieves state-of-the-art performance on complex visual tasks, outperforming proprietary models on new and existing benchmarks.
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24330
β’ PDF: https://arxiv.org/pdf/2512.24330
β’ Github: https://github.com/OpenSenseNova/SenseNova-MARS
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/sensenova/SenseNova-MARS-Data
β’ https://huggingface.co/datasets/sensenova/HR-MMSearch
==================================
For more data science resources:
β https://t.me/DataScienceT
#MultimodalAI #ReinforcementLearning #VisionLanguageModels #AgenticAI #ComputerVision
π Summary:
SenseNova-MARS empowers Vision-Language Models with interleaved visual reasoning and dynamic tool use like search and cropping via reinforcement learning. It achieves state-of-the-art performance on complex visual tasks, outperforming proprietary models on new and existing benchmarks.
πΉ Publication Date: Published on Dec 30, 2025
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2512.24330
β’ PDF: https://arxiv.org/pdf/2512.24330
β’ Github: https://github.com/OpenSenseNova/SenseNova-MARS
β¨ Datasets citing this paper:
β’ https://huggingface.co/datasets/sensenova/SenseNova-MARS-Data
β’ https://huggingface.co/datasets/sensenova/HR-MMSearch
==================================
For more data science resources:
β https://t.me/DataScienceT
#MultimodalAI #ReinforcementLearning #VisionLanguageModels #AgenticAI #ComputerVision
β€1
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β¨Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
π Summary:
Avatar Forcing creates real-time interactive talking head avatars. It uses diffusion forcing for low-latency reactions to user input and a label-free preference optimization for expressive, preferred motion, achieving 6.8x speedup.
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00664
β’ PDF: https://arxiv.org/pdf/2601.00664
β’ Project Page: https://taekyungki.github.io/AvatarForcing/
β’ Github: https://github.com/TaekyungKi/AvatarForcing
==================================
For more data science resources:
β https://t.me/DataScienceT
#AvatarGeneration #RealTimeAI #GenerativeAI #ComputerVision #AIResearch
π Summary:
Avatar Forcing creates real-time interactive talking head avatars. It uses diffusion forcing for low-latency reactions to user input and a label-free preference optimization for expressive, preferred motion, achieving 6.8x speedup.
πΉ Publication Date: Published on Jan 2
πΉ Paper Links:
β’ arXiv Page: https://arxiv.org/abs/2601.00664
β’ PDF: https://arxiv.org/pdf/2601.00664
β’ Project Page: https://taekyungki.github.io/AvatarForcing/
β’ Github: https://github.com/TaekyungKi/AvatarForcing
==================================
For more data science resources:
β https://t.me/DataScienceT
#AvatarGeneration #RealTimeAI #GenerativeAI #ComputerVision #AIResearch