Machine learning books and papers

CycleGuardian: A Framework for Automatic RespiratorySound classification Based on Improved Deep clustering and Contrastive Learning 🖥 Github: https://github.com/chumingqian/CycleGuardian 📕 Paper: https://arxiv.org/abs/2502.00734v1 🌟 Dataset: https://paperswithcode.com/dataset/icbhi-respiratory-sound-database @Machine_learn

با عرض سلام نفر سوم از مقاله زیر رو جهت همکاری نیاز داریم. این مقاله ۸ ماه داریم روش کار میکنیم. Title: Gaussian Mixture latent for Recurrent Neural Networks Basic deficiencies The problem of time series prediction analyzes patterns in past data to predict the future. Traditional machine learning algorithms, despite achieving impressive results, require manual feature selection. Automatic feature selection along with the addition of time concept in deep recurrent networks has led to the provision of more suitable solutions. The selection of feature order in deep recurrent networks leads to the provision of different results due to the use of Back-propagation. The problem of selecting feature order is an NP-complete problem. In this research, the aim is to provide a solution to improve this problem.......! هزینه مشارکت نفر سوم این مقاله ۱۰۰۰$ و ژورنال هدف Expert system میباشد. @Raminmousa @Machine_learn

DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding 13 Dec 2024 · Zhiyu Wu, Xiaokang Chen, Zizheng Pan, Xingchao Liu, Wen Liu, Damai Dai, Huazuo Gao, Yiyang Ma, Chengyue Wu, Bingxuan Wang, Zhenda Xie, Yu Wu, Kai Hu, Jiawei Wang, Yaofeng Sun, Yukun Li, Yishi Piao, Kang Guan, Aixin Liu, Xin Xie, Yuxiang You, Kai Dong, Xingkai Yu, Haowei Zhang, Liang Zhao, Yisong Wang, Chong Ruan · We present DeepSeek-VL2, an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models that significantly improves upon its predecessor, DeepSeek-VL, through two key major upgrades. For the vision component, we incorporate a dynamic tiling vision encoding strategy designed for processing high-resolution images with different aspect ratios. For the language component, we leverage #DeepSeekMoE models with the Multi-head Latent Attention mechanism, which compresses Key-Value cache into latent vectors, to enable efficient inference and high throughput. Trained on an improved vision-language dataset, DeepSeek-VL2 demonstrates superior capabilities across various tasks, including but not limited to visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. Our model series is composed of three variants: DeepSeek-VL2-Tiny, #DeepSeek-VL2-Small and DeepSeek-VL2, with 1.0B, 2.8B and 4.5B activated parameters respectively. DeepSeek-VL2 achieves competitive or state-of-the-art performance with similar or fewer activated parameters compared to existing open-source dense and MoE-based models. Codes and pre-trained models are publicly accessible at https://github.com/deepseek-ai/DeepSeek-VL2. Paper: https://arxiv.org/pdf/2412.10302v1.pdf Code: https://github.com/deepseek-ai/deepseek-vl2 Datasets: RefCOCO TextVQA MMBench DocVQA 💠 https://t.me/deep_learning_proj

Microsoft just updated their blog with 300 examples of real-world AI use cases. 📕 Article @Machine_learn

با عرض سلام تخفیف ۵۰٪ دو پکیچ یادگیری ماشین و یادگیری عمیق که شامل ۳۶ پروژه عملی در بحث پردازش تصویر و پردازش متن می باشند رو در نظر گرفتیم. دوستانی که نیاز به این دو پک دارند می تونن به بنده پیام بدن. ۱ ماه مشاوره ریکان راجع به این پروژه ها هم خواهیم داشت. 🟥🟥🟥🟥🟥🟥 @Raminmousa

Introduction to Python for Computational Science and Engineering 📚 book @Machine_learn

Demystifying Long Chain-of-Thought Reasoning in LLMs 🖥 paper 🧠 code @Machine_learn

CoSTI: Consistency Models for (a faster) Spatio-Temporal Imputation 31 Jan 2025 · Javier Solís-García, Belén Vega-Márquez, Juan A. Nepomuceno, Isabel A. Nepomuceno-Chamorro · Multivariate Time Series Imputation (MTSI) is crucial for many applications, such as healthcare monitoring and traffic management, where incomplete data can compromise decision-making. Existing state-of-the-art methods, like Denoising Diffusion Probabilistic Models (DDPMs), achieve high imputation accuracy; however, they suffer from significant computational costs and are notably time-consuming due to their iterative nature. In this work, we propose CoSTI, an innovative adaptation of Consistency Models (CMs) for the MTSI domain. CoSTI employs Consistency Training to achieve comparable imputation quality to DDPMs while drastically reducing inference times, making it more suitable for real-time applications. We evaluate CoSTI across multiple datasets and missing data scenarios, demonstrating up to a 98% reduction in imputation time with performance on par with diffusion-based models. This work bridges the gap between efficiency and accuracy in generative imputation tasks, providing a scalable solution for handling missing data in critical spatio-temporal systems. Paper: https://arxiv.org/pdf/2501.19364v1.pdf Code: https://github.com/javiersgjavi/costi @Machine_learn

Awesome-LLM-as-a-judge Survey ▪ Github 🔸https://t.me/deep_learning_proj

نفر ۵ از این پروژه همچنان خالی هست...! @Raminmousa

RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domains Paper: https://arxiv.org/pdf/2501.19205v1.pdf Code: https://github.com/camlab-ethz/rigno @Machine_learn

با عرض سلام نفر ٥ ام از پروژه جديدمون باقي مونده و ٦ جايگاه ديگه پر شدن. امكان اموزش كامل كار كدنويسي كار نحوه جمع اوري داده ها نگارش مقاله در اين كار وجود داره Project Title: MedRec: Medical recommender system for image classification without retraining Github: https://github.com/Ramin1Mousa/MedicalRec Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Impact factor: 20.8 🔺 5- 300$ جهت مشارکت می تونید به ایدی بنده پیام بدین. @Raminmousa

Free Certification Courses to Learn Data Analytics in 2025: 1. Python 🔗 https://imp.i384100.net/5gmXXo 2. SQL 🔗 https://edx.org/learn/relational-databases/stanford-university-databases-relational-databases-and-sql 3. Statistics and R 🔗 https://edx.org/learn/r-programming/harvard-university-statistics-and-r 4. Data Science: R Basics 🔗https://edx.org/learn/r-programming/harvard-university-data-science-r-basics 5. Excel and PowerBI 🔗 https://learn.microsoft.com/en-gb/training/paths/modern-analytics/ 6. Data Science: Visualization 🔗https://edx.org/learn/data-visualization/harvard-university-data-science-visualization 7. Data Science: Machine Learning 🔗https://edx.org/learn/machine-learning/harvard-university-data-science-machine-learning 8. R 🔗https://imp.i384100.net/rQqomy 9. Tableau 🔗https://imp.i384100.net/MmW9b3 10. PowerBI 🔗 https://lnkd.in/dpmnthEA 11. Data Science: Productivity Tools 🔗 https://lnkd.in/dGhPYg6N 12. Data Science: Probability 🔗https://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science 13. Mathematics 🔗http://matlabacademy.mathworks.com 14. Statistics 🔗 https://lnkd.in/df6qksMB 15. Data Visualization 🔗https://imp.i384100.net/k0X6vx 16. Machine Learning 🔗 https://imp.i384100.net/nLbkN9 17. Deep Learning 🔗 https://imp.i384100.net/R5aPOR 18. Data Science: Linear Regression 🔗https://pll.harvard.edu/course/data-science-linear-regression/2023-10 19. Data Science: Wrangling 🔗https://edx.org/learn/data-science/harvard-university-data-science-wrangling 20. Linear Algebra 🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-2-introduction-linear-models-and-matrix-algebra 21. Probability 🔗 https://pll.harvard.edu/course/data-science-probability 22. Introduction to Linear Models and Matrix Algebra 🔗https://edx.org/learn/linear-algebra/harvard-university-introduction-to-linear-models-and-matrix-algebra 23. Data Science: Capstone 🔗 https://edx.org/learn/data-science/harvard-university-data-science-capstone 24. Data Analysis 🔗 https://pll.harvard.edu/course/data-analysis-life-sciences-4-high-dimensional-data-analysis 25. IBM Data Science Professional Certificate https://imp.i384100.net/9gxbbY 26. Neural Networks and Deep Learning https://imp.i384100.net/DKrLn2 27. Supervised Machine Learning: Regression and Classification https://imp.i384100.net/g1KJEA @Machine_learn

A Little Bit of Reinforcement Learning from Human Feedback 📓 Book @Machine_learn

LLMs can see and hear without any training 30 Jan 2025 · Kumar Ashutosh, Yossi Gandelsman, Xinlei Chen, Ishan Misra, Rohit Girdhar · We present MILS: Multimodal Iterative LLM Solver, a surprisingly simple, training-free approach, to imbue multimodal capabilities into your favorite LLM. Leveraging their innate ability to perform multi-step reasoning, MILS prompts the LLM to generate candidate outputs, each of which are scored and fed back iteratively, eventually generating a solution to the task. This enables various applications that typically require training specialized models on task-specific data. In particular, we establish a new state-of-the-art on emergent zero-shot image, video and audio captioning. MILS seamlessly applies to media generation as well, discovering prompt rewrites to improve text-to-image generation, and even edit prompts for style transfer! Finally, being a gradient-free optimization approach, MILS can invert multimodal embeddings into text, enabling applications like cross-modal arithmetic. Paper: https://arxiv.org/pdf/2501.18096v1.pdf Code: https://github.com/facebookresearch/mils ✅ https://t.me/deep_learning_proj

20% از کارهای این مقاله انجام شده و این اخرین فرصت برای جوین شدن به پروژست. @Raminmousa

با عرض سلام نفر ٥ ام از پروژه جديدمون باقي مونده و ٦ جايگاه ديگه پر شدن. امكان اموزش كامل كار كدنويسي كار نحوه جمع اوري داده ها نگارش مقاله در اين كار وجود داره Project Title: MedRec: Medical recommender system for image classification without retraining Github: https://github.com/Ramin1Mousa/MedicalRec Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence Impact factor: 20.8 🔺 5- 300$ جهت مشارکت می تونید به ایدی بنده پیام بدین. @Raminmousa

با عرض سلام در يكي از پروژه هاي طبقه بندي سرطان پوست نياز به مشاركت داريم. جايگاه نفر سوم خالي مي باشد. 🔸🔻🔸🔻🔸🔻🔻 @Raminmousa