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Computer Science and Programming
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Channel specialized for advanced topics of: * Artificial intelligence, * Machine Learning, * Deep Learning, * Computer Vision, * Data Science * Python Admin: @otchebuch Memes: @memes_programming Ads: @Source_Ads, https://telega.io/c/computer_science
Mostrar más📈 Análisis del canal de Telegram Computer Science and Programming
El canal Computer Science and Programming (@computer_science_and_programming) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 142 757 suscriptores, ocupando la posición 815 en la categoría Tecnologías y Aplicaciones y el puesto 87 en la región Italia.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 142 757 suscriptores.
Según los últimos datos del 13 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -1 316, y en las últimas 24 horas de -26, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 6.13%. Durante las primeras 24 horas tras publicar, el contenido suele obtener 1.79% de reacciones respecto al total de suscriptores.
- Alcance de las publicaciones: Cada publicación recibe en promedio 8 753 visualizaciones. En el primer día suele acumular 2 559 visualizaciones.
- Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 17.
- Intereses temáticos: El contenido se centra en temas clave como sellerflash, github, developer, pricing, waybienad.
📝 Descripción y política de contenido
El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“Channel specialized for advanced topics of:
* Artificial intelligence,
* Machine Learning,
* Deep Learning,
* Computer Vision,
* Data Science
* Python
Admin: @otchebuch
Memes: @memes_programming
Ads: @Source_Ads,
https://telega.io/c/computer_sc...”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 14 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.
142 757
Suscriptores
-2624 horas
-1847 días
-1 31630 días
Archivo de publicaciones
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
Paper:
https://arxiv.org/pdf/2203.04708v2.pdf
Code:
https://github.com/suyukun666/UFO
Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation.
Github: https://github.com/164140757/scm
Paper: https://arxiv.org/abs/2207.10447v1
Dataset: https://paperswithcode.com/dataset/cub-200-2011
🏎 Instance Shadow Detection with A Single-Stage Detector
Deep framework, and an evaluation metric to approach this new task.
Github: https://github.com/stevewongv/InstanceShadowDetection
Instance Shadow Detection: https://github.com/stevewongv/SSIS
Video: https://www.youtube.com/watch?v=p0b_2SsFypw
Colab: https://colab.research.google.com/drive/1y9UpS5uA1YuoMyvYVzcKL4ltA_FDu_x0?usp=sharing
Paper: https://arxiv.org/abs/2207.04614v1
Datasets: https://paperswithcode.com/dataset/soba
@computer_science_and_programming
Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech
IMS Toucan is a toolkit for teaching, training and using state-of-the-art Speech Synthesis models.
Github: https://github.com/DigitalPhonetics/IMS-Toucan
https://github.com/rballester/tntorch
Pre-Generated Audios: https://multilingualtoucan.github.io/
Cloning prosody across speakers: https://toucanprosodycloningdemo.github.io/
Interactive Demo: https://huggingface.co/spaces/Flux9665/IMS-Toucan
Paper: https://arxiv.org/abs/2206.12229v1
@computer_science_and_programming
CVPR 2022 open access
https://openaccess.thecvf.com/CVPR2022?day=2022-06-21
MIT, Introduction to Deep Learning, 2022 Lecture series
Website:
http://introtodeeplearning.com/
Lecture:
https://www.youtube.com/watch?v=7sB052Pz0sQ&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
Github: https://github.com/kssteven418/squeezeformer
Paper: https://arxiv.org/abs/2206.00888v1
Dataset: https://paperswithcode.com/dataset/librispeech
AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition
Github: https://github.com/ShoufaChen/AdaptFormer
Paper: https://arxiv.org/abs/2205.13535v1
Dataset: https://paperswithcode.com/dataset/something-something-v2
+1RefineMask: Towards High-Quality Instance Segmentation
with Fine-Grained Features (CVPR 2021)
Paper:
https://openaccess.thecvf.com/content/CVPR2021/papers/Zhang_RefineMask_Towards_High-Quality_Instance_Segmentation_With_Fine-Grained_Features_CVPR_2021_paper.pdf
Source:
https://github.com/zhanggang001/RefineMask
@computer_science_and_programming
🧊 Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral)
Github: https://github.com/dvlab-research/focalsconv
Paper: https://arxiv.org/abs/2204.12463
Dataset: https://paperswithcode.com/dataset/nuscenes
NAFSSR: Stereo Image Super-Resolution Using NAFNet
Github: https://github.com/megvii-research/NAFNet
Paper: https://arxiv.org/abs/2204.08714v1
Demo: https://colab.research.google.com/drive/1dkO5AyktmBoWwxBwoKFUurIDn0m4qDXT?usp=sharing
Dataset: https://paperswithcode.com/dataset/kitti
💬 A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution
Github: https://github.com/mjq11302010044/tatt
Paper: https://arxiv.org/abs/2203.09388v2
Dataset: https://deepchecks.com/blog/
Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science.
https://towardsdatascience.com/ai-papers-to-read-in-2022-c6edd4302247
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
👉👉@computer_science_and_programming
323+ Open Source Pytorch Implementation Software Projects
Free and open source pytorch implementation code projects including engines, APIs, generators, and tools.
https://opensourcelibs.com/libs/pytorch-implementation
A curated list of tutorials, papers, projects, communities and more related to PyTorch:
https://www.ritchieng.com/the-incredible-pytorch/
https://github.com/ritchieng/the-incredible-pytorch
@computer_science_and_programming
✨ Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning
Github: https://github.com/sense-x/uniformer
Paper: https://arxiv.org/abs/2201.04676v1
Tasks: https://paperswithcode.com/dataset/kinetics-600
@computer_science_and_programming
An important collection of the 15 best machine learning cheat sheets.
1- Supervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf
2- Unsupervised Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf
3- Deep Learning
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf
4- Machine Learning Tips and Tricks
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf
5- Probabilities and Statistics
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf
6- Comprehensive Stanford Master Cheat Sheet
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf
7- Linear Algebra and Calculus
https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf
8- Data Science Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf
9- Keras Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf
10- Deep Learning with Keras Cheat Sheet
https://github.com/rstudio/cheatsheets/raw/master/keras.pdf
11- Visual Guide to Neural Network Infrastructures
http://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png
12- Skicit-Learn Python Cheat Sheet
https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
13- Scikit-learn Cheat Sheet: Choosing the Right Estimator
https://scikit-learn.org/stable/tutorial/machine_learning_map/
14- Tensorflow Cheat Sheet
https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf
15- Machine Learning Test Cheat Sheet
https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/
@computer_science_and_programming
Happy new year
Thank you for being with us
We appreciate your patience to science and always try to provide best content for subscribers
Page: https://d2l.ai/
PyTorch based: https://d2l.ai/d2l-en-pytorch.pdf
MXNET based: https://d2l.ai/d2l-en.pdf
Github: https://github.com/d2l-ai/d2l-en
👉👉@computer_science_and_programming
Dive into Deep Learning
Interactive deep learning book with code, math, and discussions
Implemented with NumPy/MXNet, PyTorch, and TensorFlow
Adopted at 300 universities from 55 countries
