Machine learning books and papers

🔖 ML algorithms in visualizations A useful repository that helps you understand how machine learning algorithms work – through interactive diagrams and step-by-step explanations. You can run it in your browser or locally using Docker. ⛓ Link to GitHub https://github.com/gavinkhung/machine-learning-visualized @Machine_learn

"The Mathematics of Bitcoin" is a concise work that analyzes Bitcoin from a mathematical perspective. 📊 It utilizes probability theory, stochastic processes, martingales, combinatorics, and special functions to explore the mechanisms of the Bitcoin protocol. 🧮 In particular, the authors examine the probability of double-spending, the profitability of mining, block generation, miner strategies, and the resilience of the protocol. ⛏️ If you want to delve deeper, I also recommend "Bitcoin and Cryptocurrency Technologies" from Princeton University. This is a much broader introduction to cryptographic hash functions, digital signatures, consensus, Proof of Work, mining, transactions, anonymity, security, and the incentive system in cryptocurrencies. 🎓 The Mathematics of Bitcoin: https://arxiv.org/pdf/2003.00001 Bitcoin and Cryptocurrency Technologies: https://d28rh4a8wq0iu5.cloudfront.net/bitcointech/readings/princeton_bitcoin_book.pdf @Machine_learn

🔥 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_learn

اخرین زمان سابمیت این مقاله امشب...! @Raminmousa1

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_learn

🔖 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_learn

Attention Heatmap vs Token Pruning 🔍✂️ 🔗 More: https://www.overshoot.ai/blogs/an-introduction-to-token-pruning-for-vlms #AI #MachineLearning #TokenPruning #DeepLearning #TechNews #VLM @Machine_learn

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_learn

🔖 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_learn

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_learn

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

🔖 Comprehensive Practical Course on Reinforcement Learning We've found a repository that will help you learn Reinforcement Learning, from basic concepts to advanced algorithms. The author supports the theory with practical examples using TensorFlow, making the material ideal for self-study. ⛓️ Link to GitHub https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow @Machine_learn

با عرض سلام مقاله زیر جهت واگذاری اسامی در نظر گرفته شده است Title: A Multi-Task Framework Unifying Classification and Regression forMicrogrid Power (kWh) Forecasting: Modified FEDformer Journal: IEEE transaction on soft computing Price: 2: 500$ 3: 350$ @Raminmousa1

Video-LMM Post-Training: A Deep Dive into Video Reasoning with Large Multimodal Models Read @Machine_learn

🔥 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_learn

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 @Raminmousa1

Game Theory: http://arxiv.org/abs/1512.06808 ————— #GameTheory #Gamification #Mathematics #Statistics #Probability @Machine_learn

🔥 MemGUI-Agent: An End-to-End Long-Horizon Mobile GUI Agent with Proactive Context Management 💡 The paper introduces MemGUI-Agent, a mobile GUI agent designed to address the limitations of existing agents on long-horizon tasks. Current agents struggle with retaining intermediate facts across many steps and app transitions, leading to unreliable performance. This limitation is attributed to the ReAct-style prompting approach, which passively accumulates per-step records, causing prompt explosion and dilution of critical cross-app facts. To address this issue, the authors propose MemGUI-Agent, which uses proactive context management through Context-as-Action, or ConAct. ConAct casts context management as first-class actions emitted by the same policy that selects UI actions. This approach maintains three structured context fields: folded action history, folded UI state, and recent step record, preserving critical UI facts while keeping context compact. The authors also introduce MemGUI-3K, a dataset with 2,956 trajectories and full ConAct annotations for supervised training and offline analysis. Training an 8B model on MemGUI-3K results in MemGUI-8B-SFT, an 8B MemGUI-Agent that achieves the best open-data 8B performance on MemGUI-Bench and generalizes to the out-of-distribution MobileWorld benchmark. The contributions of the paper are threefold. Firstly, it identifies the limitations of existing mobile GUI agents on long-horizon tasks and attributes them to the ReAct-style prompting approach. Secondly, it proposes MemGUI-Agent with proactive context management through ConAct, which addresses the limitations of existing agents. Finally, it introduces MemGUI-3K, a dataset for supervised training and offline analysis, and demonstrates the effectiveness of MemGUI-8B-SFT, an 8B MemGUI-Agent trained on this dataset. The code, data, and trained models will be released to facilitate further research and development. 📅 Published on Jun 18 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2606.19926 • PDF: https://arxiv.org/pdf/2606.19926 • Project Page: https://memgui-agent.github.io/ 🤖 Models citing this paper: • https://huggingface.co/lgy0404/MemGUI-8B-SFT 📊 Datasets citing this paper: • https://huggingface.co/datasets/lgy0404/MemGUI-3K ━━━━━━━━━━━━━━━━━━━━━━━━ @Machine_learn