Machine learning books and papers

با عرض سلام ٨٠٪؜ نگارش مقاله زير انجام شده است title: A survey of generative adversarial network on next generation networks:5G and 6G Networks مقاله در ابتدا در اركايو ثبت ميشه و كامل شدش براي ژورنال مربوطه فرستاده ميشه. دوستاني كه نياز دارن ميتونن در اين مقاله شركت كنند. اين مقاله فقط با سه نفر سابميت ميشه كه نفر اول خودم هستم و جايگاه دو و سوم خالي داره. @Raminmousa @Paper4money @Machine_learn

OmniGen: Unified Image Generation Paper: https://arxiv.org/pdf/2409.11340v1.pdf Code: https://github.com/vectorspacelab/omnigen Dataset: DreamBooth | MagicBrush ✅@Machine_learn

📃SOCIAL NETWORK ANALYSIS: FROM GRAPH THEORY TO APPLICATIONS 📎 Study paper ✅@Machine_learn

📃Large Language Models on Graphs: A Comprehensive Survey 📎 Study paper ✅@Machine_learn

MiniCPM-V MiniCPM-V 2.6: A GPT-4V Level MLLM for Single Image, Multi Image and Video on Your Phone                                                                     Creator: OpenBMB Stars ⭐️: 11.4k Forked By: 798 GitHub Repo: https://github.com/OpenBMB/MiniCPM-V ➖➖➖➖➖➖➖➖➖➖➖➖➖➖        Join ✅https://t.me/deep_learning_proj ✅@Machine_learn

با عرض سلام دو پکیچ یادگیری ماشین و یادگیری عمیق را برای دوستانی که می خواهند تا فرداشب با تخفیف ۵۰٪ مجدد قرار دادیم این تخفیف اخرین سری از تخفیف های این دو پکیچ می باشد 1: introduction to machine learning 2: Regression (linear and non-linear) 3: Tensorflow introduction 4: Tensorflow computaion graph 5: Tensorflow optimizer and loss function 6: Tensorflow linear and non linear regression 7: logistic regression 8: Tensorflow regression ___________ 9: introduction to traditional machine learning *10: knn and desicion tree *11: desicion tree and Naive bayes *12: desicion tree, knn, Naive bayes implementation *13: k-means *14: Guassion Mixture Model(GMM) *15: implementation K-means and GMM _ 16: introduction to Artificial Neural Network 17: Multi-level Neural Network 18: Introduction to Convolution Neural Network 19: Tensorflow Multi-level Neural Network 20:Tensorflow CNN 21:CNN image clasaification 22: Cnn text clasaification 23: Recurrent Neural Network(RNN) جهت تهیه می تونین به ایدی بنده مراجعه کنین @Raminmousa

Fundamentals of Data Engineering 📌 Book 📌Download ✅@Machine_learn

Python for OSINT. 21-day course for beginners 📚 Book ✅@Machine_learn

با عرض سلام مقاله ی زیر تماما نگارش شده و اماده سابمیت از دوستان کسی خواست نفر ۴ اش خالی هست. IEEE Geoscience and Remote Sensing Letter Impact factor 4 CiteScore 7.6 ------------------------------ Title: Enhanced-HisSegNet: An Enhanced Histagram Layered Segmentation Network for SAR Image-based Flood Segmentation ------------------------------ Abstract: Floods are among the most frequent natural disasters, causing loss of life and significant economic and environmental damage, with direct impacts on agriculture, urban infrastructure, and transportation networks. Therefore, it is crucial to accurately and efficiently identify flooded areas in the aftermath of such events. Synthetic Aperture Radar (SAR) imagery plays a vital role in this process, as water surfaces reflect less microwave energy compared to land due to their smooth texture and low surface roughness. In this study, we present a multimodal fusion strategy that enhances the existing model by Turkmenli et al. [1] through the integration of fine-tuned histograms and Deep Neural Networks (DNNs) for improved flood mapping. Specifically, we introduce fine-tuned histogram extraction layers designed for SAR data, which are integrated into Deep Segmentation Neural Networks (DSNNs). The model was tested on two real SAR datasets, with cross-dataset validation using an external cohort, representing a second innovation in our approach. Experimental results demonstrate that our model, with fine-tuned histogram layers, outperforms previous approaches by up to 4% in intersection over union (IoU) and provides a comprehensive evaluation through metrics such as Precision, Recall, Average Precision (AP), Mean Average Precision (mAP), False Positive Rate (FPR), and Mean Average Recall (mAR). Importantly, these improvements come with minimal additional learnable parameters. The code for this work will be made available at https://github.com/Mohsena1990/Enhanced-HistSegNet @Raminmousa @Machine_learn @Paper4monry

LLaMA-Omni: Seamless Speech Interaction with Large Language Models Paper: https://arxiv.org/pdf/2409.06666v1.pdf Code: https://github.com/ictnlp/llama-omni ✅@Machine_learn

🌟 GRIN MoE: Mixture-of-Experts от Microsoft. 🟢total parameters: 16x3.8B; 🟢active parameters: 6.6B; 🟢context length: 4096; 🟢number of embeddings 4096; 🟢number of layers: 32; ✅https://t.me/deep_learning_proj 🟡Arxiv 🟡Demo 🖥Github @Machine_learn

Algorithm Design and Analysis 📓 Book ✅@Machine_learn

⭐️ ثبت‌نام استارکمپ Machine Learning پیشرفته دانشکار شروع شد! 💻📊 مناسب افراد فعال در حوزه دیتا ساینس و دیتا آنالیز و که به دنبال ارتقا خود در این زمینه هستن. 🔽 مباحث این استارکمپ: 🔸Ensemble Algorithm 🔸Anomaly Detection 🔸Machine Learning Pipeline 🔸Feature Selection 💼 این دوره پروژه‌محوره با نیازهای شرکت‌ها تطابق داره تا شما رو برای بازار کار و ارتقا شغلی آماده کنه. 🤖🐍 برای این دوره باید آشنایی مقدماتی با پایتون و ماشین لرنینگ داشته باشین. ✅ ثبت‌نام و مشاوره رایگان: 🔗 https://dnkr.ir/2Mze4

فقط نفر ٤ ام باقي مونده. @Raminmousa

فقط نفر ٤ ام باقي مونده.