✨Kronos: A Foundation Model for the Language of Financial Markets
📝 Summary:
Kronos is a novel foundation model for financial K-line data. It uses a specialized tokenizer and autoregressive pre-training on a vast dataset to significantly outperform existing models in price and volatility forecasting, and synthetic data generation, establishing it as a versatile tool for f...
🔹 Publication Date: Published on Aug 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02739
• PDF: https://arxiv.org/pdf/2508.02739
• Github: https://github.com/shiyu-coder/Kronos
🔹 Models citing this paper:
• https://huggingface.co/NeoQuasar/Kronos-base
• https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base
• https://huggingface.co/NeoQuasar/Kronos-mini
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ByronWang2005/Kronos-CS2-Skins-Forecast-Demo
• https://huggingface.co/spaces/yangyang158/kronos
• https://huggingface.co/spaces/heyunfei/crypt
==================================
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#FoundationModel #FinancialAI #DeepLearning #QuantitativeFinance #Forecasting
📝 Summary:
Kronos is a novel foundation model for financial K-line data. It uses a specialized tokenizer and autoregressive pre-training on a vast dataset to significantly outperform existing models in price and volatility forecasting, and synthetic data generation, establishing it as a versatile tool for f...
🔹 Publication Date: Published on Aug 2, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2508.02739
• PDF: https://arxiv.org/pdf/2508.02739
• Github: https://github.com/shiyu-coder/Kronos
🔹 Models citing this paper:
• https://huggingface.co/NeoQuasar/Kronos-base
• https://huggingface.co/NeoQuasar/Kronos-Tokenizer-base
• https://huggingface.co/NeoQuasar/Kronos-mini
✨ Spaces citing this paper:
• https://huggingface.co/spaces/ByronWang2005/Kronos-CS2-Skins-Forecast-Demo
• https://huggingface.co/spaces/yangyang158/kronos
• https://huggingface.co/spaces/heyunfei/crypt
==================================
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#FoundationModel #FinancialAI #DeepLearning #QuantitativeFinance #Forecasting
arXiv.org
Kronos: A Foundation Model for the Language of Financial Markets
The success of large-scale pre-training paradigm, exemplified by Large Language Models (LLMs), has inspired the development of Time Series Foundation Models (TSFMs). However, their application to...
✨Guiding a Diffusion Transformer with the Internal Dynamics of Itself
📝 Summary:
This paper introduces Internal Guidance IG for diffusion models, which adds auxiliary supervision to intermediate layers during training and extrapolates outputs during sampling. This simple strategy significantly improves training efficiency and generation quality. IG achieves state-of-the-art F...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24176
• PDF: https://arxiv.org/pdf/2512.24176
• Project Page: https://zhouxingyu13.github.io/Internal-Guidance/
• Github: https://github.com/CVL-UESTC/Internal-Guidance
==================================
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#DiffusionModels #AI #DeepLearning #GenerativeAI #ComputerVision
📝 Summary:
This paper introduces Internal Guidance IG for diffusion models, which adds auxiliary supervision to intermediate layers during training and extrapolates outputs during sampling. This simple strategy significantly improves training efficiency and generation quality. IG achieves state-of-the-art F...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24176
• PDF: https://arxiv.org/pdf/2512.24176
• Project Page: https://zhouxingyu13.github.io/Internal-Guidance/
• Github: https://github.com/CVL-UESTC/Internal-Guidance
==================================
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#DiffusionModels #AI #DeepLearning #GenerativeAI #ComputerVision
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❤1
✨A unified framework for detecting point and collective anomalies in operating system logs via collaborative transformers
📝 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.
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23380
• PDF: https://arxiv.org/pdf/2512.23380
• Project Page: https://www.alarmif.com
• Github: https://github.com/NasirzadehMoh/CoLog
==================================
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✓ https://t.iss.one/DataScienceT
#AnomalyDetection #LogAnalysis #Transformers #MachineLearning #Cybersecurity
📝 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.
🔹 Publication Date: Published on Dec 29, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23380
• PDF: https://arxiv.org/pdf/2512.23380
• Project Page: https://www.alarmif.com
• Github: https://github.com/NasirzadehMoh/CoLog
==================================
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#AnomalyDetection #LogAnalysis #Transformers #MachineLearning #Cybersecurity
❤2
✨Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation
📝 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.
🔹 Publication Date: Published on Nov 21, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2411.14384
• PDF: https://arxiv.org/pdf/2411.14384
• Project Page: https://caiyuanhao1998.github.io/project/DiffusionGS/
• Github: https://github.com/caiyuanhao1998/Open-DiffusionGS
🔹 Models citing this paper:
• https://huggingface.co/CaiYuanhao/DiffusionGS
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CaiYuanhao/DiffusionGS
==================================
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✓ https://t.iss.one/DataScienceT
#3DGeneration #DiffusionModels #GaussianSplatting #ComputerVision #AIResearch
📝 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.
🔹 Publication Date: Published on Nov 21, 2024
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2411.14384
• PDF: https://arxiv.org/pdf/2411.14384
• Project Page: https://caiyuanhao1998.github.io/project/DiffusionGS/
• Github: https://github.com/caiyuanhao1998/Open-DiffusionGS
🔹 Models citing this paper:
• https://huggingface.co/CaiYuanhao/DiffusionGS
✨ Datasets citing this paper:
• https://huggingface.co/datasets/CaiYuanhao/DiffusionGS
==================================
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#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...
✨LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference
📝 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.
🔹 Publication Date: Published on Oct 8, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.09665
• PDF: https://arxiv.org/pdf/2510.09665
• Github: https://github.com/LMCache/LMCache
==================================
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#LLM #KVCache #GPU #AIInference #PerformanceOptimization
📝 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.
🔹 Publication Date: Published on Oct 8, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.09665
• PDF: https://arxiv.org/pdf/2510.09665
• Github: https://github.com/LMCache/LMCache
==================================
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#LLM #KVCache #GPU #AIInference #PerformanceOptimization
✨Dynamic Large Concept Models: Latent Reasoning in an Adaptive Semantic Space
📝 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.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24617
• PDF: https://arxiv.org/pdf/2512.24617
==================================
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✓ https://t.iss.one/DataScienceT
#AI #MachineLearning #LargeModels #RepresentationLearning #EfficientAI
📝 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.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24617
• PDF: https://arxiv.org/pdf/2512.24617
==================================
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#AI #MachineLearning #LargeModels #RepresentationLearning #EfficientAI
✨On the Role of Discreteness in Diffusion LLMs
📝 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...
🔹 Publication Date: Published on Dec 27, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22630
• PDF: https://arxiv.org/pdf/2512.22630
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 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...
🔹 Publication Date: Published on Dec 27, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.22630
• PDF: https://arxiv.org/pdf/2512.22630
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨DiffThinker: Towards Generative Multimodal Reasoning with Diffusion Models
📝 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 ...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24165
• PDF: https://arxiv.org/pdf/2512.24165
• Project Page: https://diffthinker-project.github.io/
• Github: https://github.com/lcqysl/DiffThinker
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 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 ...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24165
• PDF: https://arxiv.org/pdf/2512.24165
• Project Page: https://diffthinker-project.github.io/
• Github: https://github.com/lcqysl/DiffThinker
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨AI-native Memory 2.0: Second Me
📝 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.
🔹 Publication Date: Published on Mar 11, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.08102
• PDF: https://arxiv.org/pdf/2503.08102
• Github: https://github.com/Mindverse/Second-Me
==================================
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#AI #LLM #MemoryManagement #HCI #NLP
📝 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.
🔹 Publication Date: Published on Mar 11, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2503.08102
• PDF: https://arxiv.org/pdf/2503.08102
• Github: https://github.com/Mindverse/Second-Me
==================================
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#AI #LLM #MemoryManagement #HCI #NLP
❤2
✨Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational Modeling
📝 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 ...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23959
• PDF: https://arxiv.org/pdf/2512.23959
• Github: https://github.com/Encyclomen/HGMem
==================================
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#RAG #Hypergraphs #NLP #AI #LLM
📝 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 ...
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.23959
• PDF: https://arxiv.org/pdf/2512.23959
• Github: https://github.com/Encyclomen/HGMem
==================================
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#RAG #Hypergraphs #NLP #AI #LLM
✨Dream2Flow: Bridging Video Generation and Open-World Manipulation with 3D Object Flow
📝 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.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24766
• PDF: https://arxiv.org/pdf/2512.24766
==================================
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✓ https://t.iss.one/DataScienceT
#VideoGeneration #Robotics #3DVision #AI #ZeroShotLearning
📝 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.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24766
• PDF: https://arxiv.org/pdf/2512.24766
==================================
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#VideoGeneration #Robotics #3DVision #AI #ZeroShotLearning
✨FlowBlending: Stage-Aware Multi-Model Sampling for Fast and High-Fidelity Video Generation
📝 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.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24724
• PDF: https://arxiv.org/pdf/2512.24724
==================================
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#VideoGeneration #GenerativeAI #DeepLearning #AIResearch #ModelOptimization
📝 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.
🔹 Publication Date: Published on Dec 31, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24724
• PDF: https://arxiv.org/pdf/2512.24724
==================================
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#VideoGeneration #GenerativeAI #DeepLearning #AIResearch #ModelOptimization
🚀 Master Data Science & Programming!
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🧠 Code With Python
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💬 Data Science Chat
An active community group for discussing data challenges and networking with peers.
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🖊 Data Science Jupyter Notebooks
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https://t.iss.one/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.iss.one/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
https://t.iss.one/DataScience4
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
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❤2
✨TESO Tabu Enhanced Simulation Optimization for Noisy Black Box Problems
📝 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.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24007
• PDF: https://arxiv.org/pdf/2512.24007
• Github: https://github.com/bulentsoykan/TESO
==================================
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📝 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.
🔹 Publication Date: Published on Dec 30, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.24007
• PDF: https://arxiv.org/pdf/2512.24007
• Github: https://github.com/bulentsoykan/TESO
==================================
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This channels is for Programmers, Coders, Software Engineers.
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🚀 Master Data Science & Programming!
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✨Dolphin: Document Image Parsing via Heterogeneous Anchor Prompting
📝 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.
🔹 Publication Date: Published on May 20, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.14059
• PDF: https://arxiv.org/pdf/2505.14059
• Github: https://github.com/bytedance/dolphin
==================================
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✓ https://t.iss.one/DataScienceT
#DocumentParsing #MultimodalAI #DeepLearning #ComputerVision #AI
📝 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.
🔹 Publication Date: Published on May 20, 2025
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2505.14059
• PDF: https://arxiv.org/pdf/2505.14059
• Github: https://github.com/bytedance/dolphin
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#DocumentParsing #MultimodalAI #DeepLearning #ComputerVision #AI
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🚀 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!
🔰 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.iss.one/CodeProgrammer
🔖 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.
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https://t.iss.one/DataScience4
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━━━━━━━━━━━━━━━━━━
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.iss.one/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.iss.one/DataScienceM
This channel delivers clear, practical content for developers, covering Python, Django, Data Structures, Algorithms, and DSA – perfect for learning, coding, and mastering key programming skills.
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Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://t.iss.one/DataScienceQ
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://t.iss.one/datasets1
The first channel in Telegram that offers free Udemy coupons
https://t.iss.one/DataScienceC
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://t.iss.one/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://t.iss.one/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://t.iss.one/PythonArab
Explore the world of Data Science through Jupyter Notebooks—insights, tutorials, and tools to boost your data journey. Code, analyze, and visualize smarter with every post.
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Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://t.iss.one/DataAnalyticsX
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://t.iss.one/Python53
Professional Academic Writing & Simulation Services
https://t.iss.one/DataScienceY
━━━━━━━━━━━━━━━━━━
Admin: @HusseinSheikho
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