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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 24 518 obunachidan iborat bo'lib, Taʼlim toifasida 8 048-o'rinni va Eron mintaqasida 13 749-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 24 518 obunachiga ega bo‘ldi.
25 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -164 ga, so‘nggi 24 soatda esa -1 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 7.13% 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 1 748 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 26 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.
24 518
Obunachilar
-124 soatlar
-407 kunlar
-16430 kunlar
Postlar arxiv
Repost from Papers
يكي از بهترين موضوعات در طبقه بندي متن؛ تحليل احساس چند دامنه اي مي باشد. براي اين منظور مدلي تحت عنوان
Title: TRCAPS: The Transformer-based Capsule Approach for Persian Multi-
Domain Sentiment Analysis
طراحي كرديم كه نتايج خيلي بهتري نسبت به IndCaps داشته است.
دوستاني كه نياز به مقاله تو حوزه NLP دارن مي تونن تا اخر اين هفته داخل اين مقاله شركت كنند.
ژورنال هدف Array elsevier مي باشد.
شركت كنندگان داخل اين مقاله نياز به انجام تسك هايي نيز مي باشند.
@Raminmousa
@Machine_learn
@Paper4money
📄 Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade
📎 Study the paper
@Machine_learn
🌟 Zamba2-Instruct
🟢Zamba2-1.2B-instruct;
🟠Zamba2-2.7B-instruct.
# Clone repo
git clone https://github.com/Zyphra/transformers_zamba2.git
cd transformers_zamba2
# Install the repository & accelerate:
pip install -e .
pip install accelerate
# Inference:
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16)
user_turn_1 = "user_prompt1."
assistant_turn_1 = "assistant_prompt."
user_turn_2 = "user_prompt2."
sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}]
chat_sample = tokenizer.apply_chat_template(sample, tokenize=False)
input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
print((tokenizer.decode(outputs[0])))
🖥GitHub
@Machine_learn
Prompt Engineering Techniques: Comprehensive Repository for Development and Implementation 🖋️
📓 Github
@Machine_learn
Repost from Github LLMs
🔥 NVIDIA silently release a Llama 3.1 70B fine-tune that outperforms
GPT-4o and Claude Sonnet 3.5
Llama 3.1 Nemotron 70B Instruct a further RLHFed model on
huggingface
https://huggingface.co/collections/nvidia/llama-31-nemotron-70b-670e93cd366feea16abc13d8
✅https://t.me/deep_learning_proj
✔️ LVD-2M: A Long-take Video Dataset with Temporally Dense Captions
New pipeline for selecting high-quality long-take videos and generating temporally dense captions.
Dataset with four key features essential for training long video generation models: (1) long videos covering at least 10 seconds, (2) long-take videos without cuts, (3) large motion and diverse contents, and (4) temporally dense captions.
🖥 Github: https://github.com/silentview/lvd-2m
📕 Paper: https://arxiv.org/abs/2410.10816v1
🖥 Dataset: https://paperswithcode.com/dataset/howto100m
🔸@Machine_learn
Repost from Papers
با عرض سلام در يكي از مقالاتمون با موضوع
multimodal capsule fusion with self-attention approach for alzheimer disease classification
نياز به نفر دوم هستيم. تسك ها به صورت مشخص شده براي نفر دوم در نظر گرفته شده است. دوستاني كه ميخوان مشاركت كنن به بنده
پيام بدن با تشكر.
@Raminmousa
@Machine_learn
@Paper4money
📃Network Modeling and Control of Dynamic Disease Pathways, Review and Perspectives
📎 Study the paper
@Machine_learn
پروژه های بیشتر شبیه این ریپورت داخل این پک قرار داره. دوستانی که نیاز دارن می تونن به ایدی بنده مراجعه کنن.
@Raminmousa
Efficiently Democratizing Medical LLMs for 50 Languages via a Mixture of Language Family Experts
💻 Github: https://github.com/freedomintelligence/apollomoe
🔖 Paper: https://arxiv.org/abs/2410.10626v1
🤗 Dataset: https://paperswithcode.com/dataset/mmlu
✅@Machine_learn
با عرض سلام خيلي از دوستان در رابطي 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
