Endi mavjud! Telegram Tadqiqoti 2025 — yilning asosiy insaytlari 
Artificial Intelligence && Deep Learning
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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 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 58 029 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, 58 029 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.
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- 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.
58 029
Obunachilar
+1724 soatlar
-237 kunlar
-19330 kunlar
Postlar arxiv
Deep Learning for Object Detection: A Comprehensive Review
* Single Shot Multibox Detector (SSD) with MobileNets
* SSD with Inception V2
* Region-Based Fully Convolutional Networks (R-FCN) with Resnet 101
* Faster RCNN with Resnet 101
* Faster RCNN with Inception Resnet v2
1. join👉@DeepLearning_AI
2.https://towardsdatascience.com/deep-learning-for-object-detection-a-comprehensive-review-73930816d8d9
Good day dear subscribers.
Within this past a year we learn or still learning more about specific topics through channel.
I try with my best to provide, keep going with contemporary knowladge and practice, as well as, keep in touch with things based on #AI, #ML, #DL, #DS, #Python.
Thanks for being with us and Stay with us.
CS230 Deep Learning Lectures | Stanford Engineering
Deep Learning is one of the most highly sought after skills in AI. In this course, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.
1. join👉@DeepLearning_AI
2. https://www.newworldai.com/cs230-deep-learning-stanford-engineering/?fbclid=IwAR2BXdgS70N5wVeTdnevVL4eF_L3_l67vBetQkCOIoEazDZLeSx5TJUp6t0
FREE ONLINE COURSES
Browse the latest free online courses from HARVARD University including "CS50's Introduction to Game Development", "CS50's Web Programming with Python and JavaScript" , "CS50's Mobile App Development with React Native" and so on....
1. join👉@DeepLearning_AI
2. https://online-learning.harvard.edu/catalog/free
3D Face Reconstruction with Position Map Regression Networks 👉 (with source code)
1. join👉@DeepLearning_AI
2. https://heartbeat.fritz.ai/3d-face-reconstruction-with-position-map-regression-networks-36f0ac2d3ef1
3. https://github.com/YadiraF/PRNet
3D Face Reconstruction with Position Map Regression Networks
It doesn’t matter if you are beginner or new to machine learning or advanced researcher in the field of deep learning methods and their application, everybody can benefit of Lex Fridman’s course on Deep Learning for Self-Driving Cars.
join👉@DeepLearning_AI
https://www.newworldai.com/deep-learning-and-self-driving-cars-from-mit-lectures-01-05/?fbclid=IwAR1hxhjsHMihuDNyBvA8zycdPKv6anSkkkpGws3LKsumcdntoVTvQhp8slU
Face Estimation
join👉@DeepLearning_AI
Dataset :- http://domedb.perception.cs.cmu.edu/
Code:- https://github.com/CMU-Perceptual-Computing-Lab/openpose
Real-time and video processing object detection using Tensorflow, OpenCV and Docker.
join👉@DeepLearning_AI
https://towardsdatascience.com/real-time-and-video-processing-object-detection-using-tensorflow-opencv-and-docker-2be1694726e5
In today’s brand new tutorial you will learn how to utilize YOLO and Tiny-YOLO for near real-time object detection on the Raspberry Pi with a Movidius NCS (with source code):
join👉@DeepLearning_AI
https://www.pyimagesearch.com/2020/01/27/yolo-and-tiny-yolo-object-detection-on-the-raspberry-pi-and-movidius-ncs/
StarGAN v2: Diverse Image Synthesis for Multiple Domains
join👉@DeepLearning_AI
https://arxiv.org/abs/1912.01865
Un-official TensorFlow Implementation https://github.com/clovaai/stargan-v2
Intro to anomaly detection with OpenCV, Computer Vision, and scikit-learn 👉(with source code)
join👉@DeepLearning_AI
https://www.pyimagesearch.com/2020/01/20/intro-to-anomaly-detection-with-opencv-computer-vision-and-scikit-learn/
Lecture Notes: Regularization for Deep Learning
Join👇👇👇
@DeepLearning_AI
https://towardsdatascience.com/lecture-notes-regularization-be3e7f8e7749
Using neural networks to solve advanced mathematics equations
Facebook AI has built the first AI system that can solve advanced mathematics equations using symbolic reasoning. By developing a new way to represent complex mathematical expressions as a kind of language and then treating solutions as a translation problem for sequence-to-sequence neural networks.
January 14,2020
👉 @DeepLearning_AI👈
https://ai.facebook.com/blog/using-neural-networks-to-solve-advanced-mathematics-equations/
CS221: Artificial Intelligence: Principles And Techniques | Stanford University
What do web search, speech recognition, face recognition, machine translation, autonomous driving, and automatic scheduling have in common? These are all complex real-world problems, and the goal of artificial intelligence (AI) is to tackle these with rigorous mathematical tools.
👉@DeepLearning_AI👈
https://www.newworldai.com/cs221-artificial-intelligence-principles-and-techniques-stanford-university/
AEI: Artificial ‘Emotional’ Intelligence (source code)
👉@DeepLearning_AI👈
https://towardsdatascience.com/aei-artificial-emotional-intelligence-ea3667d8ece
TensorFlow Lite Now Faster with Mobile GPUs
Posted by the TensorFlow team
👉@DeepLearning_AI👈
https://medium.com/tensorflow/tensorflow-lite-now-faster-with-mobile-gpus-developer-preview-e15797e6dee7
Best of Machine Learning in 2019: Reddit Edition
A look at 17 of the most popular projects, research papers, demos, and more from the subreddit r/MachineLearning over the past year
👉@DeepLearning_AI👈
https://heartbeat.fritz.ai/best-of-machine-learning-in-2019-reddit-edition-5fbb676a808
