Machine learning books and papers

ReCamMaster: Camera-Controlled Generative Rendering from A Single Video Source code: https://github.com/KlingAIResearch/ReCamMaster @Machine_learn

فقط نفر دوم از این مقاله باقی مونده...!

با عرض سلام یک هفته تا سابمیت این مقاله وقت مونده. دوستانی که نیاز دارن میتونن مشارکت کنند. @Raminmousa1

This repository contains Jupyter notebooks for the O'Reilly book "Transformers: The Definitive Guide." It includes code for computer vision tasks, time series analysis, audio processing, and reinforcement learning. https://github.com/Nicolepcx/transformers-the-definitive-guide @Machine_learn

با عرض سلام برای یکی از مقالاتمون تحت عنون زیر نیازمند نفر دوم و سوم هستیم. Price: 2 --> 200$ Price 3--> 150$ Title:Skin cancer diagnosis (scd) using efficientnet-wavelet and Optimization algortithms @Raminmousa1

🔖Computer Science Fundamentals from MIT We found the textbook Mathematics for Computer Science – covering the mathematics that underlies algorithms and computer science. Logic, graphs, combinatorics, probability, induction, recurrence relations, and discrete structures – all in one place. ⛓️ Link to the textbook https://ocw.mit.edu/courses/6-042j-mathematics-for-computer-science-spring-2015/mit6_042js15_textbook.pdf @Machine_learn

☁️ 24 ساعت تست رایگان سرور ابری ویراک، حالا با اعتبار هدیه بیشتر! اگر قصد راه‌اندازی یا تمدید سرویس‌های ابری خود را دارید، الان بهترین زمان است. ✨ تا پایان مردادماه: 🎁 15% شارژ بیشتر هدیه روی اولین واریزی 🎁 10% شارژ بیشتر هدیه روی تمام واریزی‌های بعدی 🎁 24 ساعت تست رایگان فرقی نمی‌کند اولین بار است که ویراک را انتخاب می‌کنید یا از قبل همراه ما بوده‌اید؛ با هر شارژ، اعتبار بیشتری دریافت می‌کنید و همان زیرساخت قدرتمند را با هزینه کمتر در اختیار خواهید داشت. برای دریافت کد تست رایگان کلمه *«تست»* رو به آیدی زیر ارسال کنید. https://t.me/cloud_virak 👇 پس از دریافت کد تست رایگان وارد پنل VirakCloud شوید ، کد تخفیف خود را وارد نمایید و ابرک خود را بسازید: 🔗 https://B2n.ir/qy4432 ☎️ 02191555530

Matrix Calculus for Machine Learning and Beyond! — a free ebook from MIT. This is the 2025 MIT textbook by Alan Edelman, Steven G. Johnson, and Paige Bright. The book directly connects matrix calculus to modern machine learning. Inside: *   Derivatives of matrices and vectors *   Jacobian and Hessian *   Matrix decompositions *   Optimization *   Differentiation in reverse mode *   Backpropagation of error *   Automatic differentiation *   Derivatives through ODEs *   Problems focused on machine learning This is a comprehensive mathematical bridge between linear algebra, calculus, optimization, backpropagation, and machine learning. Free ebook: https://geni.us/Matrix-Calculus-Book @Machine_learn

🔖 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