Machine learning books and papers

Kanalga Telegram’da o‘tish

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 8 056-o'rinni va Eron mintaqasida 13 757-o'rinni egallagan.

📊 Auditoriya ko‘rsatkichlari va dinamika

невідомо sanasidan buyon loyiha tez o‘sib, 24 518 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.

24 518

Obunachilar

-324 soatlar

-477 kunlar

-16530 kunlar

1 663

Post ko'rishlar

~ 46524 soatlar

~ 57948 soatlar

6.78%

Muloqot nisbati

Ma'lumot yo'q

Kuniga postlar

Ads index

beta

Ma'lumot yuklanmoqda...

O'xshash kanallar

76.0K

Data Science & Machine Learning

35.0K

Age Of Geeks

20.2K

Programming Resources

17.1K

AI with Papers - Artificial Intelligence & Deep Learning

15.0K

Tensorflow(@CVision)

Ko'proq kanallar

Kirish va chiqish esdaliklari

---

Obunachilarni jalb qilish

Iyun '26

+33

1 kanalda

May '26

+45

2 kanalda

Get PRO

Aprel '26

+45

0 kanalda

Get PRO

Mart '26

0 kanalda

Get PRO

Fevral '26

+14

1 kanalda

Get PRO

Yanvar '26

+10

1 kanalda

Get PRO

Dekabr '25

+251

2 kanalda

Get PRO

Noyabr '25

+522

11 kanalda

Get PRO

Oktabr '25

+1 065

2 kanalda

Get PRO

Sentabr '25

+794

2 kanalda

Get PRO

Avgust '25

+941

11 kanalda

Get PRO

Iyul '25

+835

3 kanalda

Get PRO

Iyun '25

+734

3 kanalda

Get PRO

May '25

+956

2 kanalda

Get PRO

Aprel '25

+1 898

4 kanalda

Get PRO

Mart '25

+658

7 kanalda

Get PRO

Fevral '25

+44

4 kanalda

Get PRO

Yanvar '25

+92

8 kanalda

Get PRO

Dekabr '24

+134

5 kanalda

Get PRO

Noyabr '24

+394

13 kanalda

Get PRO

Oktabr '24

+463

6 kanalda

Get PRO

Sentabr '24

+249

4 kanalda

Get PRO

Avgust '24

+387

3 kanalda

Get PRO

Iyul '24

+220

1 kanalda

Get PRO

Iyun '24

+228

11 kanalda

Get PRO

May '24

+422

2 kanalda

Get PRO

Aprel '24

+611

11 kanalda

Get PRO

Mart '24

+731

4 kanalda

Get PRO

Fevral '24

+1 021

1 kanalda

Get PRO

Yanvar '24

+1 100

13 kanalda

Get PRO

Dekabr '23

+628

0 kanalda

Get PRO

Noyabr '23

+409

11 kanalda

Get PRO

Oktabr '23

+296

9 kanalda

Get PRO

Sentabr '23

+365

0 kanalda

Get PRO

Avgust '23

+467

0 kanalda

Get PRO

Iyul '23

+715

0 kanalda

Get PRO

Iyun '23

+268

0 kanalda

Get PRO

May '23

+107

0 kanalda

Get PRO

Aprel '23

+107

0 kanalda

Get PRO

Mart '23

+249

0 kanalda

Get PRO

Fevral '23

+110

0 kanalda

Get PRO

Yanvar '23

+163

0 kanalda

Get PRO

Dekabr '22

+118

0 kanalda

Get PRO

Noyabr '22

+165

0 kanalda

Get PRO

Oktabr '22

+101

0 kanalda

Get PRO

Sentabr '22

+177

0 kanalda

Get PRO

Avgust '22

+220

0 kanalda

Get PRO

Iyul '22

+229

0 kanalda

Get PRO

Iyun '22

+56

0 kanalda

Get PRO

May '22

+296

0 kanalda

Get PRO

Aprel '22

+411

0 kanalda

Get PRO

Mart '22

+47

0 kanalda

Get PRO

Fevral '22

+34

0 kanalda

Get PRO

Yanvar '22

+44

0 kanalda

Get PRO

Dekabr '21

+59

0 kanalda

Get PRO

Noyabr '21

+59

0 kanalda

Get PRO

Oktabr '21

+109

0 kanalda

Get PRO

Sentabr '21

+454

0 kanalda

Get PRO

Avgust '21

+251

0 kanalda

Get PRO

Iyul '21

+84

0 kanalda

Get PRO

Iyun '21

+183

0 kanalda

Get PRO

May '21

+126

0 kanalda

Get PRO

Aprel '21

+116

0 kanalda

Get PRO

Mart '21

+101

0 kanalda

Get PRO

Fevral '21

+601

0 kanalda

Get PRO

Yanvar '21

+167

0 kanalda

Get PRO

Dekabr '20

+8 834

0 kanalda

Sana	Obunachilarni jalb qilish	Esdaliklar	Kanallar
26 Iyun	0
25 Iyun	+1
24 Iyun	0
23 Iyun	0
22 Iyun	0
21 Iyun	+1
20 Iyun	+2
19 Iyun	0
18 Iyun	+1
17 Iyun	0
16 Iyun	+2
15 Iyun	+3
14 Iyun	0
13 Iyun	0
12 Iyun	+1
11 Iyun	+2
10 Iyun	+1
09 Iyun	+1
08 Iyun	+6
07 Iyun	0
06 Iyun	+3
05 Iyun	+3
04 Iyun	+5
03 Iyun	0
02 Iyun	+1
01 Iyun	0

Kanal postlari

⚡️Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds HF: https://huggingface.co/papers/2511.08892 Peoject: https://www.lumine-ai.org/ Paper: https://arxiv.org/abs/2511.08892 @Machine_learn

2	🔥 World Action Models: A Survey 💡 The paper World Action Models A Survey provides a comprehensive overview of World Action Models, which are predictive action systems that generate future states for decision making. These models balance representational richness against computational constraints, and recent developments have led to a blurring of boundaries among various related models. The survey aims to clarify these boundaries and provide a common account of the field. The authors organize existing works into two complementary views. The first view examines what each method is required to generate, including rendered futures, latent futures, and video generation free action reasoning. The second view decomposes each method into its predictive substrate, backbone, action coupling, and deployment regime. This anatomy allows for a unified discussion of key aspects such as interactability, causality, persistence, physical plausibility, and generalization. The survey reveals a consistent design pattern in World Action Models, where design choices trade representational richness against compute, memory, latency, and action label cost. The authors find that the field is moving towards methods that generate less of the future while preserving what is required for control. The survey provides a clear and unified account of the field, covering data, evaluation, and open challenges, and provides a foundation for future research in World Action Models. The main contributions of the paper are to clarify the boundaries and definitions of World Action Models, to provide a comprehensive overview of existing works, and to identify a consistent design pattern in the field. The survey also highlights the key challenges and open issues in World Action Models, including the need for more efficient and effective methods that balance representational richness against computational constraints. Overall, the paper provides a valuable resource for researchers and practitioners in the field of World Action Models, and helps to advance the state of the art in predictive action systems. 📅 Published on Jun 18 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2606.20781 • PDF: https://arxiv.org/pdf/2606.20781 • Project Page: https://world-action-models.github.io/ ━━━━━━━━━━━━━━━━━━━━━━━━ @Machine_learn	467
3	🔥 Efficient Guided Generation for Large Language Models 💡 The paper presents an efficient method for guiding large language model text generation using regular expressions and context-free grammars. The problem addressed is that guided generation can be impractical due to significant overhead. The authors propose an approach that adds minimal overhead to the token sequence generation process. This method makes guided generation feasible in practice. The approach is implemented in the open source Python library Outlines, providing a practical solution for efficient guided generation. The results indicate that the method is effective, allowing for guided generation with little to no overhead, which is a significant contribution to the field of natural language processing. 📅 Published on Jul 19, 2023 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2307.09702 • PDF: https://arxiv.org/pdf/2307.09702 ━━━━━━━━━━━━━━━━━━━━━━━━ @Machine_learn	464
4	با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲ و ۵ اش قابل اضافه شدن می باشد. دوستانی که نیاز دارن می تونن جهت ثبت اسم به ایدی بنده پیام بدن Price 2: 300$ 5:150$ @Raminmousa @Paper4money @Machine_learn	1 123
5	📃 Current Bioinformatics Tools in Precision Oncology 📎 Study paper @Machine_learn	1 100
6	با عرض سلام مقاله MedicalRec توسط بنده و دوستان ارائه شد. این مقاله جهت ارائه ی سیستم پیشنهاد دهنده مدل طبقه بندی برای تصاویر پزشکی میباشد. در ادامه ما می خواهیم MedicalRec2 را توسعه دهیم که یک مدل پیشنهاد دهنده طبقه بند و تقسیم بند در حوزه ی پزشکی می باشد. از این رو نفرات ۲ تا ۶ این مقاله را جهت مشارکت در نظر داریم. هزینه ها از قرار زیر می باشند. 2: 500$ 3: 400$ 4: 300$ 5: 250$ 6: 200$ جهت مشارکت با ایدی بنده در ارتباط باشین. @Raminmousa @Paper4money	1 748
7	+5 🎬 ساخت ویدیو • Sora • Kling • Veo • Seedance • Lumalabs 🎨 ساخت تصویر • Google Flow • Qwen Image • NanoBanana • ChatGPT Image • Grok 🎤 تقلید صدا • ElevenLabs • Fish Audio • Minimax • Descript • Respeecher 🧠 تحقیق و کاوش • ChatGPT • Gemini • Perplexity • NotebookLM • Deepseek 🗣 ساخت کاراکتر سخنگو • Heygen • Synthesia • D-ID • Hedra ━━━━━━━━━━━━━━━ 🔗 لینک ابزارها: • ChatGPT → https://chatgpt.com • Gemini → https://gemini.google.com • Perplexity → https://perplexity.ai • Deepseek → https://deepseek.com • NotebookLM → https://notebooklm.google.com • Kling → https://klingai.com • Veo → https://deepmind.google/technologies/veo • Lumalabs → https://lumalabs.ai • Sora → https://openai.com/sora • ElevenLabs → https://elevenlabs.io • Fish Audio → https://fish.audio • Descript → https://descript.com • Heygen → https://heygen.com • Synthesia → https://synthesia.io • D-ID → https://d-id.com @ai_farshad	2 593
8	تنها ۳ روز تا سابمیت این مقاله باقی مونده....!	2 064
9	برای این مقاله فقط ۵ روز وقت داریم دوستانی که نیاز دارند زودتر اقدام کنن...!	634
10	با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲، ۴ و ۵ اش قابل اضافه شدن می باشد. دوستانی که نیاز دارن می تونن جهت ثبت اسم به ایدی بنده پیام بدن Price 2: 300$ 4: 200$ 5:150$ @Raminmousa @Paper4money @Machine_learn	2 852
11	با عرض سلام یکی از مقالاتمون در حوزه ی wound image classification در ژورنال nature scientific reports ریوایزد خورده و جایگاه های ۲، ۴ و ۵ اش قابل اضافه شدن می باشد. دوستانی که نیاز دارن می تونن جهت ثبت اسم به ایدی بنده پیام بدن Price 2: 300$ 4: 200$ 3:150$ @Raminmousa @Paper4money @Machine_learn	480
12	Follow the Machine Learning with Python channel on WhatsApp: https://whatsapp.com/channel/0029VaC7Weq29753hpcggW2A	2 834
13	🔥 Awesome open-source project to learn more about Transformer Models! 🤖✨ We found this interactive website that shows you visually how transformer models work. 🌐📊 Transformer Explainer: https://poloclub.github.io/transformer-explainer/ @Machine_learn	3 002
14	❣️	3 932

Barcha postlarni ko‘rish