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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 24 506 obunachidan iborat bo'lib, Taʼlim toifasida 8 028-o'rinni va Eron mintaqasida 13 775-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 24 506 obunachiga ega bo‘ldi.
02 Iyul, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -109 ga, so‘nggi 24 soatda esa 5 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
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- Post qamrovi: Har bir post o‘rtacha 1 541 marta ko‘riladi; birinchi sutkada odatda 500 ta ko‘rish yig‘iladi.
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- Tematik yo‘nalishlar: Kontent disorder, psy, مقاله, framework, graph kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
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“Admin: @Raminmousa
ID: @Machine_learn
link: https://t.me/Machine_learn”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 03 Iyul, 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.
24 506
Obunachilar
+524 soatlar
-147 kunlar
-10930 kunlar
Postlar arxiv
🔸لیستی از کانالهای فعال در حوزههای هوشمصنوعی، علم داده , پایتون و یادگیری ماشین
هوش مصنوعی:
1️⃣ @Ai_Tv
2⃣ @HomeAi
علم داده:
1️⃣ @DataAnalysis
تحلیل داده و تصمیمگیری دادهمحور:
1️⃣ @Mr_IE
یادگیری ماشین و یادگیری عمیق :
1️⃣ @Machine_learn
2⃣ @cvision
آموزش پایتون و برنامه نویسی :
1⃣ @pythonchallenge
2⃣ @raspberry_python
3⃣ @Koolac_Org
4⃣ @Programming4all_0to100
@Machine_learn
NeRF: Neural Radiance Fields
http://www.matthewtancik.com/nerf
Tensorflow implementation: https://github.com/bmild/nerf
Paper: https://arxiv.org/abs/2003.08934v1
@Machine_learn
The TensorFlow tutorials are written as Jupyter notebooks and run directly in Google Colab—a hosted notebook environment that requires no setup.
FROM BEGINNERS TO EXPERTS
* Source Codes
* Videos
* Libraries and extensions
https://www.tensorflow.org/tutorials
@Machine_learn
In a chord diagram (or radial network), entities are arranged radially as segments with their relationships visualised by arcs that connect them. The size of the segments illustrates the numerical proportions, whilst the size of the arc illustrates the significance of the relationships1.
Chord diagrams are useful when trying to convey relationships between different entities, and they can be beautiful and eye-catching.
https://github.com/shahinrostami/chord
#python
@Machine_learn
Local-Global Video-Text Interactions for Temporal Grounding
Github: https://github.com/JonghwanMun/LGI4temporalgrounding
Paper: https://arxiv.org/abs/2004.07514
@Machine_learn
Machine Learning and Data Science free online courses to do in quarantine
A. Beginner courses
1. Machine Learning
2. Machine Learning with Python
B. Intermediate courses
3. Neural Networks and Deep Learning
4. Convolutional Neural Networks
C. Advanced course
5. Advanced Machine Learning Specialization
@Machine_learn
Regularizing Meta-Learning via Gradient Dropout
Code: https://github.com/hytseng0509/DropGrad
Paper: https://arxiv.org/abs/2004.05859
@Machine_learn
PUBG Data Analysis
https://youtu.be/sah4m-9Il7o
@Machine_learn
Hidden Markov Model - Implemented from scratch
https://zerowithdot.com/hidden-markov-model/
@Machine_learn
Python Machine Learning
Published by:
John Wiley & Sons, Inc.
@Machine_learn
Free course Deep Unsupervised Learning
https://sites.google.com/view/berkeley-cs294-158-sp20/home
@Machine_learn
TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval
Github: https://github.com/jayleicn/TVRetrieval
PyTorch implementation : https://github.com/jayleicn/TVCaption
Paper: https://arxiv.org/abs/2001.09099v1
Python Data Visualization
Cookbook Second Edition
@Machine_learn
@Machine_learn
Deep unfolding network for image super-resolution
Deep unfolding network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods.
Github: https://github.com/cszn/USRNet
Paper: https://arxiv.org/pdf/2003.10428.pdf
⚠️⚠️ANNOUNCEMENT⚠️⚠️
Learn machine Learning,AI,Data Science & more
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Learn music instruments
https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ
Learn Video Editing, photo Editing
https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ
Learn Programming Language
https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ
Learn Python
https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ
Learn Ethical Hacking
https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ
Learn More & Develop your Mind
https://t.me/joinchat/AAAAAFJoS2BjRz1pa-hdwQ
@Machine_learn
Advancing Self-Supervised and Semi-Supervised Learning with SimCLR
https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html
Code and Pretrained-Models: https://github.com/google-research/simclr
Papare: https://arxiv.org/abs/2002.05709
artificial_vision_language_processing_robotics@NetworkArtificial.pdf5.57 MB
🔸لیستی از کانالهای فعال در حوزههای هوشمصنوعی، علم داده , پایتون و یادگیری ماشین
هوش مصنوعی:
1️⃣ @Ai_Tv
2️⃣ @AI_PYTHON
3️⃣ @HomeAi
علم داده:
1️⃣ @DataAnalysis
تحلیل داده و تصمیمگیری دادهمحور:
1️⃣ @Mr_IE
یادگیری ماشین و یادگیری عمیق :
1️⃣ @Machine_learn
2⃣ @cvision
هوش تجاری و پایگاه داده:
1⃣ @BIMining
2⃣ @sql_server
آموزش پایتون و برنامه نویسی :
1⃣ @pythonchallenge
2⃣ @raspberry_python
3⃣ @Programming4all_0to100
Artificial Vision and Language Processing for Robotics
#vision
#languageprocessing
#python
@Machine_learn
