Вже доступно! Дослідження Telegram за 2025 — головні інсайти року 
Computer Science and Programming
Відкрити в Telegram
Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers * Related Courses and Ebooks With advertising offers contact:
Показати більше📈 Аналітичний огляд Telegram-каналу Computer Science and Programming
Канал Computer Science and Programming (@machinelearning_programming) у мовному сегменті Англійська є активним учасником. На даний момент спільнота об'єднує 14 846 підписників, посідаючи 8 736 місце в категорії Технології та додатки та 29 532 місце у регіоні Індія.
📊 Показники аудиторії та динаміка
З моменту свого створення невідомо, проект продемонстрував стрімке зростання, зібравши аудиторію у 14 846 підписників.
За останніми даними від 04 червня, 2026, канал демонструє стабільну активність. Хоча за останні 30 днів спостерігається зміна кількості учасників на -152, а за останні 24 години на -7, загальне охоплення залишається високим.
- Статус верифікації: Не верифікований
- Рівень залученості (ER): Середній показник залученості аудиторії становить 14.63%. Протягом перших 24 годин після публікації контент зазвичай збирає N/A% реакцій від загальної кількості підписників.
- Охоплення публікацій: В середньому кожен допис отримує 0 переглядів. Протягом першої доби публікація в середньому набирає 0 переглядів.
- Реакції та взаємодія: Аудиторія активно підтримує контент: середня кількість реакцій на один пост – 0.
- Тематичні інтереси: Контент зосереджений навколо ключових тем, таких як learning, github, engineer, quantization, detection.
📝 Опис та контентна політика
Автор описує ресурс як майданчик для висловлення суб'єктивної думки:
“Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers
* Related Courses and Ebooks
With advertising offers contact:”
Завдяки високій частоті оновлень (останні дані отримано 05 червня, 2026), канал підтримує актуальність та високий рівень охоплення публікацій. Аналітика показує, що аудиторія активно взаємодіє з контентом, що робить його важливою точкою впливу в категорії Технології та додатки.
14 846
Підписники
-724 години
-277 днів
-15230 день
Архів дописів
You don't need to spend several $𝟭𝟬𝟬𝟬𝘀 to learn Data Science.❌
Stanford University, Harvard University & Massachusetts Institute of Technology is providing free courses.💥
Here's 8 free Courses that'll teach you better than the paid ones:
1. CS50’s Introduction to Artificial Intelligence with Python (Harvard)
https://lnkd.in/d9CkkfGK
2. Data Science: Machine Learning (Harvard)
https://lnkd.in/dQ7zkCv9
3. Artificial Intelligence (MIT)
https://lnkd.in/dG5BCPen
4. Introduction to Computational Thinking and Data Science (MIT)
https://lnkd.in/ddm5Ckk9
5. Machine Learning (MIT)
https://lnkd.in/dJEjStCw
6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)
https://lnkd.in/dkpyt6qr
7. Statistical Learning (Stanford)
https://lnkd.in/dymn4hbD
8. Mining Massive Data Sets (Stanford)
📍https://lnkd.in/d2uf-FkB
Repost from Artificial Intelligence && Deep Learning
Harvard CS109A #DataScience course materials — huge collection free & open!
1. Lecture notes
2. R code, #Python notebooks
3. Lab material
4. Advanced sections
and more ...
https://harvard-iacs.github.io/2019-CS109A/pages/materials.html
It will be really useful for you
invite your friends 🌹🌹
@Deeplearning_ai
Repost from Artificial Intelligence && Deep Learning
CVPR 2022 Open Access...
Open Access versions, provided by the Computer Vision Foundation.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.
https://openaccess.thecvf.com/CVPR2022?day=2022-06-21
invite your friends 🌹🌹
@Deeplearning_ai
PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction.
Paper: https://arxiv.org/abs/2007.03858
Project Page: http://www.liuyebin.com/pamir/pamir.html
Source code: https://github.com/ZhengZerong/PaMIR
invite your friends 🌹🌹
@MachineLearning_Programming
Free courses and quests in Coding & Web3.0 with cash rewards worth of 30 000USD 👇
https://bit.ly/3FpfqHr
Use code: "machinelearning0" when signing up to get access and start learning 🙌
Repost from Artificial Intelligence && Deep Learning
Accelerate AI training in a few lines of code without changing the training setup.
https://github.com/nebuly-ai/nebulgym
invite your friends 🌹🌹
@Deeplearning_ai
Lightweight Python library for adding real-time object tracking to any detector.
https://github.com/tryolabs/norfair
@deeplearning_ai
Free programming courses & quests with cash rewards for your time in one place 📚💰
StackUp [app.stackup.dev] is a platform made for devs where you can learn about programming languages like Rust, Python, Go, Solidity, and other technologies, and earn while learning. Rewards are given after successful completion of quests.
With new campaigns every week, you can earn from a pool of over 10,000USD in cash rewards each month!
To sign up use code "machinelearning0" and gain early access: https://bit.ly/3FpfqHr
Hope it helps you to level up in the community and master different tools essential to your career as a developer! 🚀
@deeplearning_ai
Free programming courses & quests with cash rewards for your time in one place 📚💰
StackUp [app.stackup.dev] is a platform made for devs where you can learn about programming languages like Rust, Python, Go, Solidity, and other technologies, and earn while learning. Rewards are given after successful completion of quests.
With new campaigns every week, you can earn from a pool of over 10,000USD in cash rewards each month!
To sign up use code "machinelearning0" and gain early access: https://bit.ly/3FpfqHr
Hope it helps you to level up in the community and master different tools essential to your career as a developer! 🚀
Repost from Artificial Intelligence && Deep Learning
At DAIR.AI we heart open education. We are excited to share some of the best and most recent machine learning courses available on YouTube.
Hot topics:
1. Stanford CS229: Machine Learning
2. Practical Deep Learning for Coders (2020)
3. Deep Unsupervised Learning
4. Advanced NLP
5. Deep Learning for Computer Vision
6. Deep Reinforcement Learning
7. Full Stack Deep Learning
8. Self-Driving Cars (Tübingen)
https://github.com/dair-ai/ML-YouTube-Courses
invite your friends 🌹🌹
@Deeplearning_ai
If you are learning Machine Learning and wants to make end-to-end Machine Learning real-world projects, then this website can be a great resource for you.
It has project bundle(Dragon bundle) comprising more than 550+ real-world projects in ML, DL, DS, CV and NLP and PYTHON3.
More details are showned in the image above.
- Each project comes with required Dataset, complete source code(Python3) and documentation along with explanatory comments so that even beginner can understand.
- Life time access and projects are getting updates each month.
You can download the list of complete 550+ projects from our website.
Visit our website for more information.
Website Link:
https://tensorprojects.com/dragonbundle
🛸UFO: segmentation @140+ FPS🛸
👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unified framework for co-segmentation
✅Co-segmentation, co-saliency, saliency
✅Block for long-range dependencies
✅Able to reach for 140 FPS in inference
✅The new SOTA on multiple datasets
✅Source code under MIT License
[PAPER] [Source Code]
🛸UFO: segmentation @140+ FPS🛸
👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unified framework for co-segmentation
✅Co-segmentation, co-saliency, saliency
✅Block for long-range dependencies
✅Able to reach for 140 FPS in inference
✅The new SOTA on multiple datasets
✅Source code under MIT License
[PAPER] [Source Code]
EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks
https://youtu.be/cXxEwI7QbKg
invite your friends 🌹🌹
@Deeplearning_ai
Generative Occupancy Fields for 3D Surface-Aware Image Synthesis (NeurIPS 2021)
Project Page Paper Github
A lightweight vision library for performing large scale object detection & instance segmentation
Github: https://github.com/obss/sahi
Paper: https://arxiv.org/abs/2202.06934v1
Kaggle notebook: https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx
Dataset: https://paperswithcode.com/dataset/xview
invite your friends 🌹🌹
@Deeplearning_ai
PyAutoGUI is a cross-platform GUI automation Python module for human beings. Used to programmatically control the mouse & keyboard.
https://github.com/YashIndane/Call-of-Duty-
invite your friends 🌹🌹
@Deeplearning_ai
9 Best Tools to Debug Python for 2022
https://www.ittsystems.com/best-tools-to-debug-python/
invite your friends 🌹🌹
@Deeplearning_ai
.
Want to jump ahead in artificial intelligence and/or digital pathology? Excited to share that after 2+ years of development PathML 2.0 is out! An open source #computational #pathology software library created by Dana-Farber Cancer Institute/Harvard Medical School and Weill Cornell Medicine led by Massimo Loda to lower the barrier to entry to #digitalpathology and #artificialintelligence , and streamline all #imageanalysis or #deeplearning workflows.
⭐ Code: https://github.com/Dana-Farber-AIOS/pathml
