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Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 518 obunachidan iborat bo'lib, Taʼlim toifasida 8 056-o'rinni va Eron mintaqasida 13 757-o'rinni egallagan.

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Postlar arxiv

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دوستان خروجي اين كار ٣ تا مقاله خواهد بود...!

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Repost from Papers

با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم روي حوزه ي LLM مدل ها كار كنيم. این کار تحت نظر استاد Rex (Zhitao) Ying انجام میشه. link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en ۲نفر براي همکاری نياز داريم. BioPars: a pre-trained biomedical large language model for persian biomedical text mining. ١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...) ٢- پيش پردازش متن ها و تميز كردن متن ها ٣- اموزش ترنسفورمرها ي مورد نظر ٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...) دوستاني كه مايل به مشاركت هستن مي تونين بهم اطلاع بدن. هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. هزينه به ترتيب براي نفرات علاوه بر انجام تسك ها به صورت زير مي باشد. 🔹نفر سوم 500 دلار 🔺نفر چهارم 400 دلار @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

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Repost from Github LLMs

🌟 LLaMA-Mesh: 🟡Arxiv 🖥GitHub https://t.me/deep_learning_proj

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🌟 AlphaFold 3 🟡Paper 🟡Demo 🖥GitHub @Machine_learn

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Building Blocks for Theoretical Computer Science 🎓 Link @Machine_learn

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📑 Application of graph theory in liver research: A review 📎 Study paper @Machine_learn

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Repost from Papers

با عرض سلام مقاله زیر در مرحله major revision می‌باشد. نفر ۴ ام از این مقاله قابل اضافه کردن است. Abstract Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality rates. The utilization of ultrasound scans in the BUSI dataset for medical imagery pertaining to breast cancer has exhibited commendable segmentation outcomes through the application of UNet and UNet++ networks. Nevertheless, a notable drawback of these models resides in their inattention towards the temporal aspects embedded within the images. This research endeavors to enrich the UNet++ architecture by integrating LSTM layers and self-attention mechanisms to exploit temporal characteristics for segmentation purposes. Furthermore, the incorporation of a Multiscale Feature Extraction Module aims to grasp varied scale features within the UNet++. Through the amalgamation of our proposed methodology with data augmentation on the BUSI with GT dataset, an accuracy rate of 98.88%, specificity of 99.53%, precision of 95.34%, sensitivity of 91.20%, F1-score of 93.74, and Dice coefficient of 92.74% are achieved. These findings demonstrate competitiveness with cutting-edge techniques outlined in existing literature. Keywords: Attention mechanisms, BUSI dataset, Deep Learning, Feature Extraction, Multi-Scale features دوستانی که نیاز دارن به ایدی بنده پیام بدن. #Unet++ #Segmentation @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

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Probability, Random Processes, and Statistical Analysis Applications to Communications, Signal Processing, Queueing Theory and Mathematical Finance 📕 Book @Machine_learn

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Book: The Art of Data Science Authors: Roger D. Peng & Elizabeth Matsui @Machine_learn

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01. Time Series Visualization from Raw Data to Insights.pdf11.68 MB

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New o3 OpenAI model is changing the game! For a long time, ARC was seen as proof that AI models “can’t think.” The argument went: if they truly could, why do they perform so poorly on this benchmark? Well, those days are over. The o3 model demonstrates not only the ability to think but also the capability to tackle tasks once considered out of reach. 👀 Check out the full breakdown of this breakthrough: https://arcprize.org/blog/oai-o3-pub-breakthrough It might be time to rethink what AI can achieve. Looking forward to the release! @Machine_learn

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با عرض سلام در راستاي ادامه تحقيقات مشترك سعي داريم از ١ ام دي ماه روي حوزه ي LLM مدل ها كار كنيم. این کار تحت نظر استاد Rex (Zhitao) Ying انجام میشه. link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en 1نفر براي همکاری نياز داريم. BioPars: a pre-trained biomedical large language model for persian biomedical text mining. ١- مراحل اوليه: جمع اوري متن هاي فارسي بيولوژيكي از منابع (...) ٢- پيش پردازش متن ها و تميز كردن متن ها ٣- اموزش ترنسفورمرها ي مورد نظر ٤- استفاده از بردارها ي اموزش داده شده در سه تسك (...) دوستاني كه مايل به مشاركت هستن مي تونين تا ١ دي بهم اطلاع بدن. هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد. هزينه به ترتيب براي نفرات علاوه بر انجام تسك ها به صورت زير مي باشد. 🔹نفر چهارم 500 دلار @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0 🔹🔹شروع کار از امشب🔹🔹

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Gemini API Cookbook 📚 Github @Machine_learn

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مرکز تخصصی خریدوفروش مقاله +کتاب +ثبت اختراع و سایتیشن مقالات https://t.me/online24_24 https://t.me/online24_24

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Perfect Roadmap To Learn Data Science In 2024 📖 Book @Machine_learn

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دو‌ روز تا سابمیت نهایی این مقاله...!

24 518

Repost from Papers

با عرض سلام نفر سوم براي مقاله زير رو خالي داريم. Title: Alzheimer’s disease (AD) classification using swin transformer wavelet and Improved Gray Wolf Optimization (IGWO) Abstract: Alzheimer’s disease (AD) is a slow neurological disorder that destroys the thought process, and consciousness, of a human. It directly affects the development of mental ability and neurocognitive functionality. The number of patients with Alzheimer’s disease is increasing day by day, especially in old aged people, who are above 60 years of age, and, gradually, it becomes cause of their death. In this research, our goal is to present ALzSwinTNet for Alzheimer’s classification based on FMRI images. The proposed approach uses wavelet fusion in the swin transformer network to extract features. The igwo and fox optimization approaches were used to find the hyperparameters of the model. ALzSwinTNet was able to achieve an accuracy of 0.98 in 4-class classification and 1 in 2-class classification. 💠journal: https://www.sciencedirect.com/journal/expert-systems-with-applications 🔺if:7.5 هزینه مشارکت برای نفر سوم ۲۰ تومن می باشد. این هزینه صرف تسویه سرورها خواهد شد. امکان co-author نیز برای این کار هستش. @Raminmousa @Machine_learn https://t.me/+SP9l58Ta_zZmYmY0

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🌟 SmolLM2 ⏩SmolLM2-1.7B🟢SmolLM2-1.7B-Instruct🟢Instruct GGUF ⏩SmolLM2-360M🟠SmolLM2-360M-Instruct 🟠Instruct GGUF ⏩SmolLM2-135M 🟠SmolLM2-135M-Instruct 🟠Instruct GGUF от комьюнити ▶️SmolLM2-1.7B :

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM2-1.7B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)

model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("Gravity is", return_tensors="pt").to(device)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))

📌Apache 2.0 License. 🟡Demo SmolLM2 1.7B @Machine_learn

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Practitioner Guide for Creating Effective Prompts in Large Language Models 🔗 Paper @Machine_learn

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تنها نفر ۴ ام از این کار مشترک باقی مونده شروع کار ۱ دی ماه هستش. جهت همکاری به ایدی بنده پیام بدین. @Raminmousa