Computer Science and Programming
Kanalga Telegram’da o‘tish
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
Ko'proq ko'rsatish
Tarmoq:📈 Telegram kanali Computer Science and Programming analitikasi
Computer Science and Programming (@computer_science_and_programming) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 140 441 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 804-o'rinni va Italiya mintaqasida 88-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 140 441 obunachiga ega bo‘ldi.
31 Avgust, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -734 ga, so‘nggi 24 soatda esa -18 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 7.96% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining 1.94% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 11 177 marta ko‘riladi; birinchi sutkada odatda 2 728 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 14 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent sellerflash, github, developer, pricing, waybienad kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“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...”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 01 Sentabr, 2026 da olingan) sababli kanal doimo dolzarb va katta qamrovli bo‘lib qoladi. Analitika auditoriya kontent bilan faol hamkorlik qilishini, uni Texnologiyalar & Aralashmalar toifasidagi muhim ta’sir nuqtasiga aylantirishini ko‘rsatadi.
140 441
Obunachilar
-1824 soatlar
-2617 kunlar
-73430 kunlar
Postlar arxiv
💬 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
OBJECT-AWARE CROPPING FOR SELF-SUPERVISED LEARNING
Paper:
https://arxiv.org/pdf/2112.00319v1.pdf
Github:
https://github.com/shlokk/object-cropping-ssl
👉👉@computer_science_and_programming
Object-aware cropping, a simple, fast and highly effective data augmentation alternative to random scene cropping for SELF-SUPERVISED LEARNING
Another state-of-the-art archtecture for Vision tasks:
Github: https://github.com/sail-sg/poolformer
Paper: https://arxiv.org/abs/2111.11418
Datasets: ImageNet, COCO, Ade20k
Colab: https://colab.research.google.com/github/sail-sg/poolformer/blob/main/misc/poolformer_demo.ipynb
👉👉@computer_science_and_programming
ESPnet: end-to-end text-to-speech processing toolkit
ESPnet2-TTS: Extending the Edge of TTS Research
Github: https://github.com/espnet/espnet
Docs: https://espnet.github.io/espnet/
Paper: https://arxiv.org/abs/2110.07840v1
Dataset: https://paperswithcode.com/dataset/vctk
One of the best reference book is definately "Deep Learning with Python" (1st edition) by François Chollet (creator of Keras)
Deep Learning with Python (2nd edition) has been released with 500 pages of code examples, theory, context, practical tips...
Book:
https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras
For online reading:
https://livebook.manning.com/book/deep-learning-with-python-second-edition/chapter-1/
Jupyter notebooks on Github:
https://github.com/fchollet/deep-learning-with-python-notebooks
👉👉@computer_science_and_programming
Under review as a conference paper at ICLR 2022
8-BIT OPTIMIZERS VIA BLOCK-WISE QUANTIZATION
Paper:
https://arxiv.org/abs/2110.02861
Github:
https://github.com/facebookresearch/bitsandbytes
Video:
https://www.youtube.com/watch?v=IxrlHAJtqKE
Documentation:
https://bitsandbytes.readthedocs.io/en/latest/
👉@computer_science_and_programming
8-bit optimizers – a replacement for regular optimizers. 🚀, 75% less memory, same with upwards trend, no hyperparam tuning needed Input symbol for numbers: #Lightweight, #LessMemory
PASS: Pictures without humAns for Self-Supervised Pretraining
PASS is a large-scale image dataset that does not include any humans, human parts, or other personally identifiable information
Github
https://github.com/yukimasano/PASS
Paper
https://arxiv.org/abs/2109.13228v1
Dataset
https://paperswithcode.com/dataset/pass
Documentation
https://www.robots.ox.ac.uk/~vgg/research/pass/
