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Computer Science and Programming
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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:
Mostrar más📈 Análisis del canal de Telegram Computer Science and Programming
El canal Computer Science and Programming (@machinelearning_programming) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 14 843 suscriptores, ocupando la posición 8 736 en la categoría Tecnologías y Aplicaciones y el puesto 29 532 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 14 843 suscriptores.
Según los últimos datos del 04 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -152, y en las últimas 24 horas de -7, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 14.63%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 0 visualizaciones. En el primer día suele acumular 0 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 0.
- Intereses temáticos: El contenido se centra en temas clave como learning, github, engineer, quantization, detection.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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:”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 05 junio, 2026), el canal mantiene la vigencia y un amplio alcance. La analítica demuestra que la audiencia interactúa activamente con el contenido, lo que lo convierte en un punto de referencia dentro de la categoría Tecnologías y Aplicaciones.
14 843
Suscriptores
-724 horas
-277 días
-15230 días
Archivo de publicaciones
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)
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3. Artificial Intelligence (MIT)
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4. Introduction to Computational Thinking and Data Science (MIT)
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5. Machine Learning (MIT)
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6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)
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7. Statistical Learning (Stanford)
https://lnkd.in/dymn4hbD
8. Mining Massive Data Sets (Stanford)
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Repost from Artificial Intelligence && Deep Learning
Harvard CS109A #DataScience course materials — huge collection free & open!
1. Lecture notes
2. R code, #Python notebooks
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It will be really useful for you
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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
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@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
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@MachineLearning_Programming
Free courses and quests in Coding & Web3.0 with cash rewards worth of 30 000USD 👇
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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
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@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.
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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
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@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.
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Website Link:
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🛸UFO: segmentation @140+ FPS🛸
👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one!
𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unified framework for co-segmentation
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✅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
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@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-
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@Deeplearning_ai
9 Best Tools to Debug Python for 2022
https://www.ittsystems.com/best-tools-to-debug-python/
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@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
