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Machine learning books and papers
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Admin: @Raminmousa ID: @Machine_learn link: https://t.me/Machine_learn
Ko'proq ko'rsatish📈 Telegram kanali Machine learning books and papers analitikasi
Machine learning books and papers (@machine_learn) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 24 517 obunachidan iborat bo'lib, Taʼlim toifasida 8 056-o'rinni va Eron mintaqasida 13 757-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 24 517 obunachiga ega bo‘ldi.
24 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -165 ga, so‘nggi 24 soatda esa -3 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 6.78% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.90% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 1 663 marta ko‘riladi; birinchi sutkada odatda 465 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 1 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent disorder, psy, مقاله, framework, graph kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 25 Iyun, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Taʼlim toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
24 517
Obunachilar
-324 soatlar
-477 kunlar
-16530 kunlar
Postlar arxiv
📃 Large language model to multimodal large language model: A journey to shape the biological macromolecules to biological sciences and medicine
📓 Journal: Molecular Therapy Nucleic Acids (I.F.=6.5)
📎 Study the paper
@Machine_learn
Repost from Papers
با عرض سلام مقاله زیر در مرحله major revision میباشد. نفر ۴ ام از این مقاله قابل اضافه کردن.
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.
@Raminmousa
@Machine_learn
https://t.me/+SP9l58Ta_zZmYmY0
Repost from Papers
با عرض سلام دوستان كه مي خوان توي تيم هاي paper ما شركت كنن موضوعات زير رو مي خواهيم جلو ببريم.
1: survey on whole slide image
▫️
2: Proposed a new model for enrergy efficiency in deep image classification models
Authers: 2, 3, 4 🔺
3:BioPars: a pretrained biomedical large language
model for persian biomedical text mining
Authors: 5🔺
4: Air quality prediction by hybrid deep learning and machine learning models
Authors:4🔺
در تمامی این موارد نیاز به انجام تسک و پرداخت هزینه سرور ها می باشیم.
@Raminmousa
Automating the Search for Artificial Life with Foundation Models
paper: https://arxiv.org/pdf/2412.17799v1.pdf
Code: https://github.com/sakanaai/asal
@Machine_learn
📽 Introduction to Network Analysis using NetworkX
🎞 Watch
@Machine_learn
📃A Survey of Graph Neural Networks for Social Recommender Systems
📎 Study paper
@Machine_learn
هزینه نهایی برای این کار رو به ۲۵ میلیون کاهش دادیم برای نفر ۵ ...!🔥
Repost from Papers
با عرض سلام پروژه Biopars رو شروع كرديم نفر ٥ ام از اين مقاله رو نياز داريم.
این کار تحت نظر استاد
Rex (Zhitao) Ying
انجام میشه.
link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en
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 (...)
هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد.
دوستاني كه نياز دارن مي تونن به تيم ما اضافه بشن
🔸🔸🔸🔸🔸
@Raminmousa
⚡️ NeuZip
▶️
# Install from PyPI
pip install neuzip
# Use Neuzip for Pytorch model
model: torch.nn.Module = # your model
+ manager = neuzip.Manager()
+ model = manager.convert(model)
🟡Arxiv
🖥GitHub
@Machine_learn
🌟 🌟 OuteTTS-0.2-500M
# Install from PyPI
pip install outetts
# Interface Usage
import outetts
# Configure the model
model_config = outetts.HFModelConfig_v1(
model_path="OuteAI/OuteTTS-0.2-500M",
language="en", # Supported languages in v0.2: en, zh, ja, ko
)
# Initialize the interface
interface = outetts.InterfaceHF(model_version="0.2", cfg=model_config)
# Optional: Create a speaker profile (use a 10-15 second audio clip)
speaker = interface.create_speaker(
audio_path="path/to/audio/file",
transcript="Transcription of the audio file."
)
# Optional: Load speaker from default presets
interface.print_default_speakers()
speaker = interface.load_default_speaker(name="male_1")
output = interface.generate(
text="%Prompt Text%%.",
temperature=0.1,
repetition_penalty=1.1,
max_length=4096,
# Optional: Use a speaker profile
speaker=speaker,
)
# Save the synthesized speech to a file
output.save("output.wav")
🟡Demo
🖥GitHub
@Machine_learn
Repost from Papers
با عرض سلام پروژه Biopars رو شروع كرديم نفر ٥ ام از اين مقاله رو نياز داريم.
این کار تحت نظر استاد
Rex (Zhitao) Ying
انجام میشه.
link: https://scholar.google.com.au/citations?user=6fqNXooAAAAJ&hl=en
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 (...)
هزينه سرور به ازاي هر ساعت ١.٢ دلار مي باشد. و حدود ٢ هزار ساعت براي اموزش مدل زباني نياز ميباشد.
دوستاني كه نياز دارن مي تونن به تيم ما اضافه بشن
🔸🔸🔸🔸🔸
@Raminmousa
Lecture notes: mathematics for artificial intelligence
📕 Link
@Machine_learn
با عرض سلام خيلي از دوستان در رابطه با طراحي صفر تا صد پروژه هاي ديپ از بنده سوال پرسيدن داخل پك زير ٣٦ پروژه رو با جزئيات شرح دادم:
1-Deep Learning Basic
-01_Introduction
--01_How_TensorFlow_Works
2-Classification apparel
-Classification apparel double capsule
-Classification apparel double cnn
3-ALZHEIMERS USING CNN(ResNet)
4-Fake News (Covid-19 dataset)
-Multi-channel
-3DCNN model
-Base line+ Char CNN
-Fake News Covid CapsuleNet
5-3DCNN Fake News
6-recommender systems
-GRU+LSTM MovieLens
7-Multi-Domain Sentiment Analysis
-Dranziera CapsuleNet
-Dranziera CNN Multi-channel
-Dranziera LSTM
8-Persian Multi-Domain SA
-Bi-GRU Capsule Net
-Multi-CNN
9-Recommendation system
-Factorization Recommender, Ranking Factorization Recommender, Item Similarity Recommender (turicreate)
-SVD, SVD++, NMF, Slope One, k-NN, Centered k-NN, k-NN Baseline, Co-Clustering(surprise)
10-NihX-Ray
-optimized CNN on FullDataset Nih-Xray
-MobileNet
-Transfer learning
-Capsule Network on FullDataset Nih-Xray
دوستاني كه نياز به اين پروژه ها دارن ميتونن با بنده در ارتباط باشن.
@Raminmousa
@Machine_learn
