Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Artificial Intelligence && Deep Learning
Kanalga Telegram’da o‘tish
Channel for who have a passion for - * Artificial Intelligence * Machine Learning * Deep Learning * Data Science * Computer vision * Image Processing * Research Papers With advertising offers contact:
Ko'proq ko'rsatish📈 Telegram kanali Artificial Intelligence && Deep Learning analitikasi
Artificial Intelligence && Deep Learning (@deeplearning_ai) Ingliz til segmentidagi kanali faol ishtirokchi. Hozirda hamjamiyat 58 023 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 2 289-o'rinni va Hindiston mintaqasida 6 003-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 58 023 obunachiga ega bo‘ldi.
24 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -193 ga, so‘nggi 24 soatda esa 17 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 9.42% darajada jalb etiladi. Nashrdan keyingi dastlabki 24 soatda kontent odatda umumiy obunachilar sonining N/A% ini tashkil etuvchi reaksiyalarni to‘playdi.
- Post qamrovi: Har bir post o‘rtacha 5 467 marta ko‘riladi; birinchi sutkada odatda 0 ta ko‘rish yig‘iladi.
- Reaksiyalar va o‘zaro ta’sir: Auditoriya faol: har bir postga o‘rtacha 16 ta reaksiya keladi.
- Tematik yo‘nalishlar: Kontent github, learning, estimation, dataset, engineer kabi asosiy mavzularga jamlangan.
📝 Tavsif va kontent siyosati
Muallif resursni shaxsiy fikrni ifoda etish maydoni sifatida ta’riflaydi:
“Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers
With advertising offers contact:”
Yuqori yangilanish chastotasi (oxirgi ma’lumot 25 Iyun, 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.
58 023
Obunachilar
+1724 soatlar
-237 kunlar
-19330 kunlar
Postlar arxiv
This is a list of awesome articles about object detection. If you want to read the paper according to time
https://github.com/amusi/awesome-object-detection
https://t.me/MachineLearning_Programming
👉https://t.me/DeepLearning_ai
Object Detection and Tracking in 2020 (15 min read)
1. Code for Object Tracking
2. Selective Search Segmentation
3. paper: (Selective Search Segmentation)
https://blog.netcetera.com/object-detection-and-tracking-in-2020-f10fb6ff9af3
👉https://t.me/DeepLearning_ai
FrankMocap: A Fast Monocular 3D Hand and Body Motion Capture by Regression and Integration.
LINK JOIN US
1. [Paper] ==> https://arxiv.org/pdf/2008.08324.pdf
2. [Video]==>https://www.youtube.com/watch?v=HXTK5ro9kGc&feature=youtu.be
3. [Code]==> https://github.com/facebookresearch/frankmocap
👉https://t.me/DeepLearning_ai
Machine Learning Crash Course features a series of lessons with video lectures, real-world case studies, and hands-on practice exercises.(FREE)
https://developers.google.com/machine-learning/crash-course
👉https://t.me/DeepLearning_ai
Top 25 Computer Vision Project Ideas for 2020
1. Edge Detection
2. Photo Sketching
3. Detecting Contours
4. Collage Mosaic Generator
5. Barcode and QR Code Scanner
6. Face Detection
7. Blur the Face
8. Image Segmentation
9. Human Counting with OpenCV
10. Colour Detection
.....
.......
https://data-flair.training/blogs/computer-vision-project-ideas/
👉https://t.me/DeepLearning_ai
Here's a list of top 100 deep learning Github trending repositories.
Date: 02-02-2020 compared to 09-01-2019
Note: This will be updated regularly.
https://github.com/mbadry1/Top-Deep-Learning
👉https://t.me/DeepLearning_ai
Self-supervised Representation Learning in Computer Vision — Part 2
https://medium.com/analytics-vidhya/self-supervised-representation-learning-in-computer-vision-part-2-8254aaee937c
👉https://t.me/DeepLearning_ai
Cracking the Coding Interview, 6th Edition is here to help you through this process, teaching you what you need to know and enabling you to perform at your very best. I've coached and interviewed hundreds of software engineers. The result is this book.
* 189 programming interview questions, ranging from the basics to the trickiest algorithm problems.
* A walk-through of how to derive each solution, so that you can learn how to get there yourself.
* Hints on how to solve each of the 189 questions, just like what you would get in a real interview.
* Five proven strategies to tackle algorithm questions, so that you can solve questions you haven't seen.
* Extensive coverage of essential topics, such as big O time, data structures, and core algorithms.
* A behind the scenes look at how top companies like Google and Facebook hire developers.
* Techniques to prepare for and ace the soft side of the interview: behavioral questions.
* For interviewers and companies: details on what makes a good interview question
Dive Into Deep Learning
August 2020 and FREE version!!! D2L is the 987-page book that Amazon scientists have compiled over the past two years and has finally been completed... an interactive and ' open source book ' with code, math and discussions.
What makes this book unique is that it was created with Jupyter Notebook and with the idea of ′′ Learning with Practice "... that is, the book in its entirety consists of executable code with adaptations in PyTorch, TensorFlow and MXNet.
1. Free PDF Download: https://d2l.ai/d2l-en.pdf
2. Download the book in 'notebook' format to read and execute locally from web site: https://d2l.ai
3. https://t.me/DeepLearning_ai
Bookshelf for Machine Learning, Deep Learning, and related topics
1. Machine Leaning and Deep Learning (50 books)
2. Python Books
3. Math Books for Machine Learning (19 books)
4. NLP Books(11 books)
5. Computer Vision (CV) Book
6. Reinforcement Learning Books
7. Speech Processing
8. cheatsheets
https://github.com/loveunk/Deep-learning-books
👉https://t.me/DeepLearning_ai
Using Flask to optimize performance with Mask R-CNN segmentation(with source code)
How to improve Mask R-CNN segmentation performance using a Flask web service.
https://medium.com/medialesson/using-flask-to-optimize-performance-with-mask-r-cnn-segmentation-39752f153029
👉https://t.me/DeepLearning_ai
If you want to do some reading on machine learning and AI, then this is the right project for you. It has many Jupyter notebooks on the basics of deep learning and machine learning in Python.
https://github.com/ageron/handson-ml
👉https://t.me/DeepLearning_ai
23 Amazing Deep Learning Project Ideas [Source Code Included]
https://data-flair.training/blogs/deep-learning-project-ideas/
👉https://t.me/DeepLearning_ai
12 Papers You Should Read to Understand Object Detection in the Deep Learning Era
https://towardsdatascience.com/12-papers-you-should-read-to-understand-object-detection-in-the-deep-learning-era-3390d4a28891
👉https://t.me/DeepLearning_ai
Comprehensive Python Cheatsheet
https://gto76.github.io/python-cheatsheet/
👉https://t.me/DeepLearning_ai
Image Object Detection — TensorFlow 2 Object Detection API
https://medium.com/swlh/image-object-detection-tensorflow-2-object-detection-api-af7244d4c34e
https://t.me/DeepLearning_ai
https://t.me/MachineLearning_Programming
All 57 of our deep learning tutorials to support Keras/TensorFlow 2!
These tutorials are 100% free for you or anyone else in the world to access, read, and learn from — we never put our blog posts behind paywalls (unlike Medium blogs, for instance).
https://www.pyimagesearch.com/category/keras-and-tensorflow/
https://t.me/DeepLearning_ai
https://t.me/MachineLearning_Programming
All 57 of our deep learning tutorials to support Keras/TensorFlow 2!
These tutorials are 100% free for you or anyone else in the world to access, read, and learn from — we never put our blog posts behind paywalls (unlike Medium blogs, for instance).
1. Join. @DeepLearning_ai
https://www.pyimagesearch.com/category/keras-and-tensorflow/
YOLOv5 is Here: State-of-the-Art Object Detection at 140 FPS
