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Machine Learning with Python
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
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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Code With Python
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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PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
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Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
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Udemy Coupons | Courses
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ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
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Data Science Chat
An active community group for discussing data challenges and networking with peers.
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Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab

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Data Science Jupyter Notebooks
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 | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
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Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
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Research Papers
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✨Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models

📝 Summary:
Promptomatix automates LLM prompt optimization, transforming natural language into high-quality prompts without manual tuning. This framework improves performance and efficiency across tasks, reducing prompt length and computational overhead.

🔹 Publication Date: Published on Jul 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2507.14241
• PDF: https://arxiv.org/pdf/2507.14241
• Github: https://github.com/SalesforceAIResearch/promptomatix

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#LLM #PromptEngineering #AI #MachineLearning #AIOptimization

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PyData Careers | Quiz
Python Data Science jobs, interview tips, and career insights for aspiring professionals.
https://news.1rj.ru/str/DataScienceQ

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Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
https://news.1rj.ru/str/datasets1

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Udemy Coupons | Courses
The first channel in Telegram that offers free Udemy coupons
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ML Research Hub
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://news.1rj.ru/str/DataScienceT

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Data Science Chat
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9

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Python Arab| بايثون عربي
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/PythonArab

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Free Online Courses | Videos
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
https://news.1rj.ru/str/DataScienceV

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Data Analytics
Dive into the world of Data Analytics – uncover insights, explore trends, and master data-driven decision making.
https://news.1rj.ru/str/DataAnalyticsX

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Learn Python Hub
Master Python with step-by-step courses – from basics to advanced projects and practical applications.
https://news.1rj.ru/str/Python53

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Research Papers
Professional Academic Writing & Simulation Services
https://news.1rj.ru/str/DataScienceY

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ML Research Hub

✨DentalGPT: Incentivizing Multimodal Complex Reasoning in Dentistry

📝 Summary:
DentalGPT is a specialized dental multimodal LLM. It improves fine-grained visual understanding and reasoning using a large dataset and reinforcement learning. DentalGPT achieves superior performance in dental disease classification and VQA.

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11558
• PDF: https://arxiv.org/pdf/2512.11558

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#DentalGPT #DentistryAI #LLM #MultimodalAI #HealthcareTech

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✨SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder

📝 Summary:
SVG-T2I enables high-quality text-to-image synthesis directly in the Visual Foundation Model feature domain. This scaled framework achieves competitive performance without a variational autoencoder, validating VFM representations for generative tasks.

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11749
• PDF: https://arxiv.org/pdf/2512.11749
• Github: https://github.com/KlingTeam/SVG-T2I

🔹 Models citing this paper:
• https://huggingface.co/KlingTeam/SVG-T2I

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#TextToImage #DiffusionModels #GenerativeAI #VisualFoundationModels #DeepLearning

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✨V-RGBX: Video Editing with Accurate Controls over Intrinsic Properties

📝 Summary:
V-RGBX is an end-to-end framework for intrinsic-aware video editing. It combines video inverse rendering with photorealistic synthesis and keyframe editing of intrinsic properties. This allows consistent, physically plausible video manipulation, like relighting or object appearance changes.

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11799
• PDF: https://arxiv.org/pdf/2512.11799
• Project Page: https://aleafy.github.io/vrgbx/
• Github: https://github.com/Aleafy/V-RGBX

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#VideoEditing #ComputerVision #InverseRendering #NeuralRendering #Graphics

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✨The N-Body Problem: Parallel Execution from Single-Person Egocentric Video

📝 Summary:
A model learns to parallelize tasks from a single egocentric video by addressing spatial and object conflicts, achieving improved action coverage and reduced collisions. AI-generated summary Humans ca...

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11393
• PDF: https://arxiv.org/pdf/2512.11393
• Project Page: https://zhifanzhu.github.io/ego-nbody/

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#AI #DataScience #MachineLearning #HuggingFace #Research

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✨PersonaLive! Expressive Portrait Image Animation for Live Streaming

📝 Summary:
PersonaLive is a diffusion framework for real-time portrait animation, overcoming latency issues in live streaming. It uses multi-stage training, implicit signals for motion control, and appearance distillation for efficiency. This achieves state-of-the-art performance with up to 7-22x speedup ov...

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11253
• PDF: https://arxiv.org/pdf/2512.11253
• Github: https://github.com/GVCLab/PersonaLive

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#PortraitAnimation #LiveStreaming #DiffusionModels #RealtimeAI #ComputerVision

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✨Structure From Tracking: Distilling Structure-Preserving Motion for Video Generation

📝 Summary:
SAM2VideoX improves realistic video motion by distilling structure-preserving priors from a tracking model into a bidirectional diffusion model. It uses novel feature fusion and local alignment, achieving significant performance gains over prior methods.

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11792
• PDF: https://arxiv.org/pdf/2512.11792
• Project Page: https://sam2videox.github.io/

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#VideoGeneration #DiffusionModels #ComputerVision #DeepLearning #MotionTracking

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✨Exploring MLLM-Diffusion Information Transfer with MetaCanvas

📝 Summary:
MetaCanvas uses MLLMs as latent-space planners for diffusion models to enable precise and structured image and video generation. This approach bridges the gap between multimodal understanding and generation, outperforming global-conditioning methods.

🔹 Publication Date: Published on Dec 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.11464
• PDF: https://arxiv.org/pdf/2512.11464

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#MLLM #DiffusionModels #GenerativeAI #ComputerVision #AIResearch

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✨EgoX: Egocentric Video Generation from a Single Exocentric Video

📝 Summary:
EgoX generates egocentric videos from single exocentric inputs. It uses video diffusion models with LoRA adaptation, unified conditioning, and geometry-guided self-attention for coherent and realistic results.

🔹 Publication Date: Published on Dec 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.08269
• PDF: https://arxiv.org/pdf/2512.08269
• Project Page: https://keh0t0.github.io/EgoX/
• Github: https://github.com/KEH0T0/EgoX

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#EgocentricVideo #VideoGeneration #DiffusionModels #ComputerVision #DeepLearning

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186 views05:02

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✨Sliding Window Attention Adaptation

📝 Summary:
Sliding Window Attention Adaptation SWAA allows pretrained LLMs to use efficient sliding window attention for long contexts without retraining. SWAA combines five adaptation methods, with specific synergistic combinations effectively recovering original long-context performance.

🔹 Publication Date: Published on Dec 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10411
• PDF: https://arxiv.org/pdf/2512.10411

🔹 Models citing this paper:
• https://huggingface.co/yuyijiong/Qwen3-SWA-adaptation

✨ Datasets citing this paper:
• https://huggingface.co/datasets/yuyijiong/LongMemEval_24k

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#LLMs #SlidingWindowAttention #LongContextAI #NLP #AIResearch

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✨MeshSplatting: Differentiable Rendering with Opaque Meshes

📝 Summary:
MeshSplatting is a novel mesh-based method for real-time novel view synthesis. It uses differentiable rendering to optimize geometry and appearance, producing high-quality meshes that integrate with AR/VR pipelines. It outperforms prior methods in quality, speed, and memory.

🔹 Publication Date: Published on Dec 7

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.06818
• PDF: https://arxiv.org/pdf/2512.06818
• Project Page: https://meshsplatting.github.io/
• Github: https://github.com/meshsplatting/mesh-splatting

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#DifferentiableRendering #NovelViewSynthesis #ComputerGraphics #ARVR #3DRendering

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✨CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images

📝 Summary:
This work quantifies uncertainty in landmark-based chest X-ray segmentation using hybrid neural networks. It derives latent and predictive uncertainty measures, showing they identify unreliable predictions. The paper also releases CheXmask-U, a large dataset with per-node uncertainty estimates.

🔹 Publication Date: Published on Dec 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10715
• PDF: https://arxiv.org/pdf/2512.10715

✨ Datasets citing this paper:
• https://huggingface.co/datasets/mcosarinsky/CheXmask-U

✨ Spaces citing this paper:
• https://huggingface.co/spaces/mcosarinsky/CheXmask-U

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#CheXmaskU #MedicalImaging #UncertaintyQuantification #DeepLearning #XraySegmentation

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✨LEO-RobotAgent: A General-purpose Robotic Agent for Language-driven Embodied Operator

📝 Summary:
LEO-RobotAgent is a general-purpose language-driven framework that uses large language models to enable various robot types to complete complex tasks. It enhances human-robot interaction and task planning, demonstrating strong generalization, robustness, and efficiency across different scenarios.

🔹 Publication Date: Published on Dec 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10605
• PDF: https://arxiv.org/pdf/2512.10605
• Github: https://github.com/LegendLeoChen/LEO-RobotAgent

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#Robotics #LLM #HumanRobotInteraction #EmbodiedAI #AI

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✨SWE-SQL: Illuminating LLM Pathways to Solve User SQL Issues in Real-World Applications

📝 Summary:
SWE-SQL introduces BIRD-CRITIC, a new benchmark for SQL issue debugging, and Six-Gym, a training environment using f-Plan Boosting. Their open-source Bird-Fixer agent surpasses proprietary LLMs like GPT-4.1 in performance, democratizing advanced SQL-debugging capabilities.

🔹 Publication Date: Published on Jun 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2506.18951
• PDF: https://arxiv.org/pdf/2506.18951
• Project Page: https://bird-critic.github.io
• Github: https://github.com/bird-bench/BIRD-CRITIC-1

✨ Datasets citing this paper:
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-flash-exp
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-open
• https://huggingface.co/datasets/birdsql/bird-critic-1.0-postgresql

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#SQL #LLM #AI #Debugging #OpenSource

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✨Scaling Behavior of Discrete Diffusion Language Models

📝 Summary:
Research on discrete diffusion language models DLMs shows their scaling behavior depends on noise type. Uniform diffusion is more parameter and data efficient than masked diffusion, making it promising for data-bound settings. A 10B parameter model confirmed this.

🔹 Publication Date: Published on Dec 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10858
• PDF: https://arxiv.org/pdf/2512.10858
• Github: https://github.com/dvruette/gidd-easydel

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#DiffusionModels #LanguageModels #NLP #AIResearch #DeepLearning

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✨Sharp Monocular View Synthesis in Less Than a Second

📝 Summary:
SHARP synthesizes photorealistic 3D views from a single image using a 3D Gaussian representation. It achieves state-of-the-art quality with rapid processing, taking less than a second, and supports metric camera movements.

🔹 Publication Date: Published on Dec 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.10685
• PDF: https://arxiv.org/pdf/2512.10685
• Project Page: https://apple.github.io/ml-sharp/
• Github: https://github.com/apple/ml-sharp

🔹 Models citing this paper:
• https://huggingface.co/apple/Sharp

✨ Spaces citing this paper:
• https://huggingface.co/spaces/ronedgecomb/ml-sharp

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#ViewSynthesis #3DVision #ComputerVision #RealtimeAI #GaussianSplats

121 views12:05

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✨Fairy2i: Training Complex LLMs from Real LLMs with All Parameters in {pm 1, pm i}

📝 Summary:
Fairy2i converts pre-trained real-valued LLMs to a complex form, enabling efficient low-bit quantization while reusing existing checkpoints. It achieves near full-precision performance for LLaMA-2 7B at 2-bit, significantly outperforming real-valued binary methods.

🔹 Publication Date: Published on Dec 2

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2512.02901
• PDF: https://arxiv.org/pdf/2512.02901
• Github: https://github.com/PKULab1806/Fairy2i-W2

🔹 Models citing this paper:
• https://huggingface.co/PKU-DS-LAB/Fairy2i-W2

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#LLM #Quantization #ModelCompression #DeepLearning #AIResearch

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