tgindex

Machine learning books and papers

описание

Admin: @Raminmousa1 ID: @Machine_learn link: https://t.me/Machine_learn

← к каналу@Machine_learn
24 463
подписчиков
Охват к подписчикам
8,5%
ERR
Реакции к просмотрам
0,16%
73 на 22 постов
Пересылки к просмотрам
1,31%
603
Постов в день
0,3
всего 25

Где отзываются чаще

доля реакций к просмотрам
  • 29 июл.10 GitHub repositories that are worth checking out for an AI engineer 🤖 1. Hands-On AI Engineering 🛠️ A collection of AI applications and agent systems with practical use cases of LLM. 👉 https://github.com/Sumanth077/Hands-On-AI-Engineering 2. Hands-On Large Language Models 📘 👉 https://github.com/HandsOnLLM/Hands-On-Large-Language-Models 3. AI Agents for Beginners 🎓 👉 https://github.com/microsoft/ai-agents-for-beginners 4. GenAI Agents 🤖 👉 https://github.com/NirDiamant/GenAI_Agents 5. Made With ML 🚀 👉 https://github.com/GokuMohandas/Made-With-ML 6. Learn Harness Engineering ⚙️ 👉 https://github.com/walkinglabs/learn-harness-engineering 7. AutoResearch 🔬 👉 https://github.com/karpathy/autoresearch 8. Designing Machine Learning Systems 📚 👉 https://github.com/chiphuyen/dmls-book 9. Awesome LLM Inference ⚡ 👉 https://github.com/xlite-dev/Awesome-LLM-Inference 10. LLM Course 🗺️ 👉 https://github.com/mlabonne/llm-course @Machine_learn0,42%
  • 2 авг.🔖 One of the most useful books on Agentic AI This is not just a textbook, but a comprehensive overview of modern LLMs, model training, RL, inference, quality assessment, and building AI agents. It's an excellent option to get a holistic picture and understand which topics deserve deeper study. ⛓️ Link to the book https://arxiv.org/abs/2606.24937 @Machine_learn0,33%
  • 6 авг.هر هفته با یک موضوع تحقیقی موضوع :تولید داده های سری زمانی با استفاده از شبکه های عصبی تخاصمی در شبکه های هوشمند #Thesis #proposed_research @Raminmousa1 @Machine_learn0,31%
  • 13 авг.🔥 8 skills = 8 free certifications >>> AI (Microsoft) - https://learn.microsoft.com/en-us/training/paths/get-started-artificial-intelligence/ Deep learning (NVIDIA) - https://learn.nvidia.com/en-us/training/self-paced-courses Data science (IBM) - https://skillsbuild.org/students/course-catalog/data-science Data Analyst (Microsoft) - https://learn.microsoft.com/en-us/training/paths/data-analytics-microsoft/ Python (Microsoft) - https://learn.microsoft.com/en-us/shows/intro-to-python-development/ SQL (Infosys) - https://www.coursejoiner.com/freeonlinecourses/infosys-free-certification-course-9/ Java (Infosys) - https://www.coursejoiner.com/uncategorized/infosys-launched-free-java-certification-course/ Cloud computing (AWS) - https://explore.skillbuilder.aws/learn/course/134/aws-cloud-practitioner-essentials @Machine_learn0,30%
  • 2 авг.Maths, CS & AI Compendium: A free textbook for aspiring AI/ML engineers 🚀 A large open-source compendium on mathematics, computer science, and AI has gone viral on GitHub. The project already has around 6.3K stars. 📚 The author positions it as a "non-traditional textbook" for practitioners: less dry notation, more intuition, connections between topics, and real-world context. 📖 It contains 20 chapters: * Vectors, matrices, calculus * Statistics and probability * Machine learning and deep learning * NLP, computer vision, audio/speech * Multimodal learning and autonomous systems * GNN, OS, algorithms * Production engineering, GPU/SIMD * AI inference, ML systems design, and applied AI 💡 This is a great resource for those who want to not just "learn ML," but to build a solid foundation: mathematics → CS → ML systems → modern AI. 🔗 GitHub: https://github.com/HenryNdubuaku/maths-cs-ai-compendium @Machine_learn0,22%
  • 4 авг.🔖 Learning Data Science through interactive examples One of the most useful repositories for those who want to better understand machine learning. It transforms complex concepts into visual experiments: you can study models, change parameters, and immediately see the results. ⛓ Link to GitHub https://github.com/GeostatsGuy/DataScienceInteractivePython @Machine_learn0,22%
  • 22 янв. 2025 г.без подписи0,20%
  • 20 июл.🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨ We found this interactive website that shows you visually how transformer models work. 🌐📊 Transformer Explainer: https://poloclub.github.io/transformer-explainer/ @Machine_learn0,18%
  • 11 июл.Game Theory: http://arxiv.org/abs/1512.06808 ————— #GameTheory #Gamification #Mathematics #Statistics #Probability @Machine_learn0,17%
  • 14 июл.با عرض سلام اکانت @Raminmousa به دلیل جابجایی در زمان لاگین تلگرام تعلیق شده و در تلاش برای رفع این مشکلیم. دوستانی که باهام پروژه دارن لطفا با این ایدیم در ارتباط باشن @Raminmousa10,16%
  • 2 авг.با عرض سلام سه موضوع زیر جهت نگارش مقالات مدنظر داریم. که در هر سه مقاله به دو جایگاه نیاز داریم. مقالات کاملا مشارکتی هست و علاوه بر تقبل هزینه کار نیز باید انجام بشه. 1: Survey on knowledge graph and large language models _ auth2: 300$ _auth3:200$ 2: Survey on challenges of large language models _ auth2: 300$ _auth3:200$ 3: New learning model for skin cancer detection _ auth2: 300$ _auth3:200$ جهت مشارکت میتونین با ایدی بنده در ارتباط باشین. زمان شروع هر مقاله یک هفته بعد از تشکیل تیم. @Raminmousa1 @Machine_learn0,16%
  • 9 авг.با عرض سلام در مقاله زیر جهت سابمیت نیاز به نفر دوم داریم Title: FFChurn: Fusion Former for Customer Churn Classification Based on Transformer, FEDformer, and Informer Abstract: Customer churn prediction is a key issue in customer relationship management that directly impacts organizational profitability and has created challenges for researchers and organizations. Machine learning (ML), Ensemble Learning (EL), and Deep Learning (DL) models have achieved comparable results on this problem. In this study, Fusion Former was introduced, integrating the FEDformer, Informer, and Transformer architectures to simultaneously extract local features and long-term dependencies. The pipeline for this model includes denoising with a wavelet transform, Min-Max normalization, and hybrid adaptive feature selection based on mutual information (MI), recursive feature elimination (RFE), and the Boruta algorithm. Four different versions of the model, including binary and ternary object fusion, were evaluated on two datasets. The results showed that the Fusion Former (FED+INF+Transformer) model, with F1 scores of 0.9876 on the Dataset 1 and 0.8887 on the Dataset 2, outperformed classical machine learning models, multilayer neural networks, and other binary combinations. Also, the sensitivity analysis of hyperparameters, which included changes in the cost function, batch size, and dropout size, methods for dealing with data imbalance, which included Smote, TMG-GAN, Ib-gan, T-SMOTE approaches, and the effect of feature selection, which included four methods: MI, RFE, Boruta, and Adaptive FS (Boruta+MI+RFE), confirmed the superiority and relative stability of the proposed model. Price:250$ @Raminmousa1 @Machine_learn0,15%