✨TurboDiffusion: Accelerating Video Diffusion Models by 100-200 Times
📝 Summary:
TurboDiffusion significantly accelerates video generation by 100-200x while maintaining quality. It achieves this speedup through attention acceleration, step distillation, and W8A8 quantization. Experiments confirm the substantial speedup on a single GPU.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16093
• PDF: https://jt-zhang.github.io/files/TurboDiffusion_Technical_Report.pdf
• Project Page: https://github.com/thu-ml/TurboDiffusion
• Github: https://github.com/thu-ml/TurboDiffusion
🔹 Models citing this paper:
• https://huggingface.co/TurboDiffusion/TurboWan2.2-I2V-A14B-720P
• https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-1.3B-480P
• https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-14B-720P
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
TurboDiffusion significantly accelerates video generation by 100-200x while maintaining quality. It achieves this speedup through attention acceleration, step distillation, and W8A8 quantization. Experiments confirm the substantial speedup on a single GPU.
🔹 Publication Date: Published on Dec 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.16093
• PDF: https://jt-zhang.github.io/files/TurboDiffusion_Technical_Report.pdf
• Project Page: https://github.com/thu-ml/TurboDiffusion
• Github: https://github.com/thu-ml/TurboDiffusion
🔹 Models citing this paper:
• https://huggingface.co/TurboDiffusion/TurboWan2.2-I2V-A14B-720P
• https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-1.3B-480P
• https://huggingface.co/TurboDiffusion/TurboWan2.1-T2V-14B-720P
==================================
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❤2
✨HiStream: Efficient High-Resolution Video Generation via Redundancy-Eliminated Streaming
📝 Summary:
High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To a...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21338
• PDF: https://arxiv.org/pdf/2512.21338
• Project Page: http://haonanqiu.com/projects/HiStream.html
• Github: https://github.com/arthur-qiu/HiStream
==================================
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📝 Summary:
High-resolution video generation, while crucial for digital media and film, is computationally bottlenecked by the quadratic complexity of diffusion models, making practical inference infeasible. To a...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21338
• PDF: https://arxiv.org/pdf/2512.21338
• Project Page: http://haonanqiu.com/projects/HiStream.html
• Github: https://github.com/arthur-qiu/HiStream
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨Beyond Memorization: A Multi-Modal Ordinal Regression Benchmark to Expose Popularity Bias in Vision-Language Models
📝 Summary:
VLMs exhibit a significant popularity bias, performing better on famous items via memorization rather than general understanding. We introduce YearGuessr, a large multi-modal dataset and benchmark, confirming VLMs struggle with unrecognized subjects.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21337
• PDF: https://arxiv.org/pdf/2512.21337
• Project Page: https://sytwu.github.io/BeyondMemo/
• Github: https://sytwu.github.io/BeyondMemo/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
VLMs exhibit a significant popularity bias, performing better on famous items via memorization rather than general understanding. We introduce YearGuessr, a large multi-modal dataset and benchmark, confirming VLMs struggle with unrecognized subjects.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21337
• PDF: https://arxiv.org/pdf/2512.21337
• Project Page: https://sytwu.github.io/BeyondMemo/
• Github: https://sytwu.github.io/BeyondMemo/
==================================
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❤1
✨Learning from Next-Frame Prediction: Autoregressive Video Modeling Encodes Effective Representations
📝 Summary:
Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregressive (AR) generative models like GPT have revolutioniz...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21004
• PDF: https://arxiv.org/pdf/2512.21004
• Github: https://github.com/Singularity0104/NExT-Vid
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Recent advances in pretraining general foundation models have significantly improved performance across diverse downstream tasks. While autoregressive (AR) generative models like GPT have revolutioniz...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21004
• PDF: https://arxiv.org/pdf/2512.21004
• Github: https://github.com/Singularity0104/NExT-Vid
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
✨TokSuite: Measuring the Impact of Tokenizer Choice on Language Model Behavior
📝 Summary:
Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs). Despite the importance of tokenization, its role in LM performance and behavior is po...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20757
• PDF: https://arxiv.org/pdf/2512.20757
• Github: https://github.com/r-three/Tokenizers
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Tokenizers provide the fundamental basis through which text is represented and processed by language models (LMs). Despite the importance of tokenization, its role in LM performance and behavior is po...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20757
• PDF: https://arxiv.org/pdf/2512.20757
• Github: https://github.com/r-three/Tokenizers
==================================
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✨DreaMontage: Arbitrary Frame-Guided One-Shot Video Generation
📝 Summary:
DreaMontage is a framework for generating seamless, expressive, long-duration one-shot videos from diverse inputs. It integrates an intermediate-conditioning DiT, a tailored DPO for smoothness, and a segment-wise auto-regressive inference strategy for long sequences.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21252
• PDF: https://arxiv.org/pdf/2512.21252
• Project Page: https://dreamontage.github.io/DreaMontage/
• Github: https://dreamontage.github.io/DreaMontage/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
DreaMontage is a framework for generating seamless, expressive, long-duration one-shot videos from diverse inputs. It integrates an intermediate-conditioning DiT, a tailored DPO for smoothness, and a segment-wise auto-regressive inference strategy for long sequences.
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21252
• PDF: https://arxiv.org/pdf/2512.21252
• Project Page: https://dreamontage.github.io/DreaMontage/
• Github: https://dreamontage.github.io/DreaMontage/
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
🔥 NEW YEAR 2026 – PREMIUM
nature papers: 400$
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nature papers: 400$
Q1 and Q2 papers 300$
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Doctoral thesis (complete) 500$
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✨Multi-hop Reasoning via Early Knowledge Alignment
📝 Summary:
Early Knowledge Alignment EKA improves iterative RAG by aligning LLMs with relevant knowledge before planning. This enhances retrieval, reduces errors, and boosts performance and efficiency.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20144
• PDF: https://arxiv.org/pdf/2512.20144
==================================
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#MultiHopReasoning #LLM #RAG #KnowledgeAlignment #AI
📝 Summary:
Early Knowledge Alignment EKA improves iterative RAG by aligning LLMs with relevant knowledge before planning. This enhances retrieval, reduces errors, and boosts performance and efficiency.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20144
• PDF: https://arxiv.org/pdf/2512.20144
==================================
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#MultiHopReasoning #LLM #RAG #KnowledgeAlignment #AI
✨SWE-EVO: Benchmarking Coding Agents in Long-Horizon Software Evolution Scenarios
📝 Summary:
SWE-EVO is a new benchmark for AI coding agents that evaluates them on long-horizon, multi-step software evolution tasks across many files. It reveals a significant gap in current models abilities, with even top models achieving only 21 percent resolution. This highlights their struggle with sust...
🔹 Publication Date: Published on Dec 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18470
• PDF: https://arxiv.org/pdf/2512.18470
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Fsoft-AIC/SWE-EVO
==================================
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#AICoding #SoftwareEvolution #Benchmarking #LLMs #AIResearch
📝 Summary:
SWE-EVO is a new benchmark for AI coding agents that evaluates them on long-horizon, multi-step software evolution tasks across many files. It reveals a significant gap in current models abilities, with even top models achieving only 21 percent resolution. This highlights their struggle with sust...
🔹 Publication Date: Published on Dec 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18470
• PDF: https://arxiv.org/pdf/2512.18470
✨ Datasets citing this paper:
• https://huggingface.co/datasets/Fsoft-AIC/SWE-EVO
==================================
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#AICoding #SoftwareEvolution #Benchmarking #LLMs #AIResearch
❤2
🚀 Master Data Science & Programming!
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ML Engineers: NVIDIA has released a guide for beginners on fine-tuning LLMs using Unsloth.
The guide covers:
- training methods: LoRA, FFT, RL
- when and why to do fine-tuning, real use cases
- how much data and VRAM are required
- how to train locally on DGX Spark, RTX graphics cards, and more
Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/
👉 https://news.1rj.ru/str/DataScienceT
The guide covers:
- training methods: LoRA, FFT, RL
- when and why to do fine-tuning, real use cases
- how much data and VRAM are required
- how to train locally on DGX Spark, RTX graphics cards, and more
Guide: https://blogs.nvidia.com/blog/rtx-ai-garage-fine-tuning-unsloth-dgx-spark/
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❤4
✨Latent Implicit Visual Reasoning
📝 Summary:
Large Multimodal Models struggle with visual reasoning due to their text-centric nature and limitations of prior methods. This paper introduces a task-agnostic mechanism for LMMs to discover and use visual reasoning tokens without explicit supervision. The approach achieves state-of-the-art resul...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21218
• PDF: https://arxiv.org/pdf/2512.21218
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LMMs #VisualReasoning #AI #ComputerVision #DeepLearning
📝 Summary:
Large Multimodal Models struggle with visual reasoning due to their text-centric nature and limitations of prior methods. This paper introduces a task-agnostic mechanism for LMMs to discover and use visual reasoning tokens without explicit supervision. The approach achieves state-of-the-art resul...
🔹 Publication Date: Published on Dec 24
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.21218
• PDF: https://arxiv.org/pdf/2512.21218
==================================
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✓ https://news.1rj.ru/str/DataScienceT
#LMMs #VisualReasoning #AI #ComputerVision #DeepLearning
❤1
✨Schoenfeld's Anatomy of Mathematical Reasoning by Language Models
📝 Summary:
This paper introduces ThinkARM, a framework based on Schoenfelds Episode Theory, to abstract LLM reasoning traces into functional steps. It reveals distinct thinking dynamics and structural differences in models solving math problems, with exploration being key for correctness. This makes LLM rea...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19995
• PDF: https://arxiv.org/pdf/2512.19995
• Github: https://github.com/MingLiiii/ThinkARM
==================================
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#LLM #AIReasoning #MathematicalReasoning #AI #MachineLearning
📝 Summary:
This paper introduces ThinkARM, a framework based on Schoenfelds Episode Theory, to abstract LLM reasoning traces into functional steps. It reveals distinct thinking dynamics and structural differences in models solving math problems, with exploration being key for correctness. This makes LLM rea...
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19995
• PDF: https://arxiv.org/pdf/2512.19995
• Github: https://github.com/MingLiiii/ThinkARM
==================================
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#LLM #AIReasoning #MathematicalReasoning #AI #MachineLearning
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✨Spatia: Video Generation with Updatable Spatial Memory
📝 Summary:
Spatia is a video generation framework that improves long-term consistency by using an updatable 3D scene point cloud as persistent spatial memory. It iteratively generates video clips and updates this memory via visual SLAM, enabling realistic videos and 3D-aware interactive editing.
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15716
• PDF: https://arxiv.org/pdf/2512.15716
• Project Page: https://zhaojingjing713.github.io/Spatia/
• Github: https://github.com/ZhaoJingjing713/Spatia
==================================
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#VideoGeneration #GenerativeAI #ComputerVision #3DReconstruction #SLAM
📝 Summary:
Spatia is a video generation framework that improves long-term consistency by using an updatable 3D scene point cloud as persistent spatial memory. It iteratively generates video clips and updates this memory via visual SLAM, enabling realistic videos and 3D-aware interactive editing.
🔹 Publication Date: Published on Dec 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.15716
• PDF: https://arxiv.org/pdf/2512.15716
• Project Page: https://zhaojingjing713.github.io/Spatia/
• Github: https://github.com/ZhaoJingjing713/Spatia
==================================
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#VideoGeneration #GenerativeAI #ComputerVision #3DReconstruction #SLAM
❤1
✨VA-π: Variational Policy Alignment for Pixel-Aware Autoregressive Generation
📝 Summary:
VA-$\pi$ optimizes autoregressive visual generators using a pixel-space objective to improve image quality and performance without retraining tokenizers or using external rewards. AI-generated summary...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19680
• PDF: https://arxiv.org/pdf/2512.19680
• Project Page: https://lil-shake.github.io/va-pi.github.io/
• Github: https://github.com/Lil-Shake/VA-Pi
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
VA-$\pi$ optimizes autoregressive visual generators using a pixel-space objective to improve image quality and performance without retraining tokenizers or using external rewards. AI-generated summary...
🔹 Publication Date: Published on Dec 22
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19680
• PDF: https://arxiv.org/pdf/2512.19680
• Project Page: https://lil-shake.github.io/va-pi.github.io/
• Github: https://github.com/Lil-Shake/VA-Pi
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨GTR-Turbo: Merged Checkpoint is Secretly a Free Teacher for Agentic VLM Training
📝 Summary:
Multi-turn reinforcement learning (RL) for multi-modal agents built upon vision-language models (VLMs) is hampered by sparse rewards and long-horizon credit assignment. Recent methods densify the rewa...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13043
• PDF: https://arxiv.org/pdf/2512.13043
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
📝 Summary:
Multi-turn reinforcement learning (RL) for multi-modal agents built upon vision-language models (VLMs) is hampered by sparse rewards and long-horizon credit assignment. Recent methods densify the rewa...
🔹 Publication Date: Published on Dec 15
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.13043
• PDF: https://arxiv.org/pdf/2512.13043
==================================
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#AI #DataScience #MachineLearning #HuggingFace #Research
❤1
✨How Much 3D Do Video Foundation Models Encode?
📝 Summary:
A new framework quantifies 3D understanding in Video Foundation Models VidFMs. VidFMs, trained only on video, show strong 3D awareness, often surpassing expert 3D models, providing insights for 3D AI.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19949
• PDF: https://arxiv.org/pdf/2512.19949
• Project Page: https://vidfm-3d-probe.github.io/
• Github: https://vidfm-3d-probe.github.io
==================================
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#VideoFoundationModels #3DUnderstanding #ComputerVision #AIResearch #DeepLearning
📝 Summary:
A new framework quantifies 3D understanding in Video Foundation Models VidFMs. VidFMs, trained only on video, show strong 3D awareness, often surpassing expert 3D models, providing insights for 3D AI.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.19949
• PDF: https://arxiv.org/pdf/2512.19949
• Project Page: https://vidfm-3d-probe.github.io/
• Github: https://vidfm-3d-probe.github.io
==================================
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#VideoFoundationModels #3DUnderstanding #ComputerVision #AIResearch #DeepLearning
❤2
✨Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
📝 Summary:
AR models face inefficient exploration and sparse rewards in RL. Internal RL uses a higher-order model to learn temporal abstraction controllers. This enables efficient learning from sparse rewards where standard RL fails.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20605
• PDF: https://arxiv.org/pdf/2512.20605
==================================
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#ReinforcementLearning #HierarchicalRL #AutoregressiveModels #MachineLearning #ArtificialIntelligence
📝 Summary:
AR models face inefficient exploration and sparse rewards in RL. Internal RL uses a higher-order model to learn temporal abstraction controllers. This enables efficient learning from sparse rewards where standard RL fails.
🔹 Publication Date: Published on Dec 23
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.20605
• PDF: https://arxiv.org/pdf/2512.20605
==================================
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#ReinforcementLearning #HierarchicalRL #AutoregressiveModels #MachineLearning #ArtificialIntelligence
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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://news.1rj.ru/str/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://news.1rj.ru/str/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://news.1rj.ru/str/DataScience4
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https://news.1rj.ru/str/DataScienceQ
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https://news.1rj.ru/str/datasets1
The first channel in Telegram that offers free Udemy coupons
https://news.1rj.ru/str/DataScienceC
Advancing research in Machine Learning – practical insights, tools, and techniques for researchers.
https://news.1rj.ru/str/DataScienceT
An active community group for discussing data challenges and networking with peers.
https://news.1rj.ru/str/DataScience9
The largest Arabic-speaking group for Python developers to share knowledge and help.
https://news.1rj.ru/str/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.
https://news.1rj.ru/str/DataScienceN
Free online courses covering data science, machine learning, analytics, programming, and essential skills for learners.
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https://news.1rj.ru/str/Python53
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https://news.1rj.ru/str/DataScienceY
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Admin: @HusseinSheikho
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