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 58 024 obunachidan iborat bo'lib, Texnologiyalar & Aralashmalar toifasida 2 297-o'rinni va Hindiston mintaqasida 6 023-o'rinni egallagan.
📊 Auditoriya ko‘rsatkichlari va dinamika
невідомо sanasidan buyon loyiha tez o‘sib, 58 024 obunachiga ega bo‘ldi.
23 Iyun, 2026 dagi oxirgi ma’lumotlarga ko‘ra kanal barqaror faollikka ega. Oxirgi 30 kunda obunachilar soni -218 ga, so‘nggi 24 soatda esa -10 ga o‘zgardi va umumiy qamrov yuqori darajada qolmoqda.
- Tasdiqlash holati: Tasdiqlanmagan
- Jalb etish (ER): Auditoriya o‘rtacha 8.90% 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 5 163 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 15 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 24 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.
58 024
Obunachilar
-1024 soatlar
-557 kunlar
-21830 kunlar
Postlar arxiv
Segment Anything in High Quality
We propose HQ-SAM to upgrade SAM for high-quality zero-shot segmentation. Refer to our paper for more details. Our code and models will be released in two weeks. Stay tuned!
https://github.com/syscv/sam-hq
@deeplearning_ai
🚨 FREE GIFTS ALERT 🚨
Want to get up-to-date with the AI landscape in 5min?
I created the Byte-Sized AI Newsletter as my way of staying on track every week, and it’s 🆓!
🔥 Specially for Programmers, I’ve also partnered with sponsors to gift you some free gifts when you subscribe:
1️⃣ An AI-powered coding skills assessment report (worth $39!)
2️⃣ Free Notion Templates + Discount codes for AI add-on features
3️⃣ Fun eBook titled “5 Theories on The Future of AI”
👉 CLICK HERE TO SUBSCRIBE AND GET YOUR FREE GIFTS!
🚨 FREE GIFTS ALERT 🚨
Want to get up-to-date with the AI landscape in 5min?
I created the Byte-Sized AI Newsletter as my way of staying on track every week, and it’s 🆓!
🔥 Specially for Programmers, I’ve also partnered with sponsors to gift you some free gifts when you subscribe:
1️⃣ An AI-powered coding skills assessment report (worth $39!)
2️⃣ Free Notion Templates + Discount codes for AI add-on features
3️⃣ Fun eBook titled “5 Theories on The Future of AI”
👉 CLICK HERE TO SUBSCRIBE AND GET YOUR FREE GIFTS!
Machine Learning Operations (MLOps) Masterclass
🏆 Unlock your full potential with MLOps Masterclass
Learn to Design ML Pipelines to Build, Train,Deploy and Monitor your Machine learning models in a real-time production environment.
Register Now👇
https://bit.ly/MLOps-masterclass
Why you shouldn't miss this Masterclass?
✔️ 15+ hands-on exercises.
✔️ 2 Real-life industry projects.
✔️Dedicated mentoring sessions from industry experts.
✔️ 10 hours session consisting of theory + Hands-on.
✔️One-on-One Debugging Session (Optional)
👨💼 Who Should Attend? 👩💼
This masterclass is perfect for Data scientists, ML engineers, Software engineers, and DevOps professionals.
Schedule:
May 27th (Sat) & 28th (Sun)
Highlights of this Masterclass:
▪️MLOps Introduction
▪️Getting started with AWS for Machine Learning
▪️AWS SageMaker Studio
▪️CI/CD Tools
▪️AWS MLOps Tools
▪️AWS MLOps - Build, Train & deploy ML Model
🔥 Limited Seats Available!
☎️ Contact:
Sarath Kumar
+918940876397 / +918778033930
Stanford CS330: Deep Multi-Task and Meta Learning.
While deep learning has achieved remarkable success in many problems such as image classification, natural language processing, and speech recognition, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes:
- self-supervised pre-training for downstream few-shot learning and transfer learning
meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly
- curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer
GET Free Course Link
Join us: @deeplarning_ai
Big News! Meta just released Segment Anything, a new AI model that can "cut out" any object, in any image/video, with a single click.
The model is designed and trained to be promptable, so it can transfer zero-shot to new image distributions and tasks.
https://segment-anything.com/
Check out https://AlphaSignal.ai to get a weekly summary of the top breakthroughs in Machine Learning.
@deeplearning_ai
"A panda is playing guitar on times square"
Text2Video-Zero
Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generators
Paper: https://arxiv.org/abs/2303.13439
Video Result: video result link
Source code: https://github.com/picsart-ai-research/text2video-zero
Join us: @deeplarning_ai
MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications.
2023 lectures are starting in just one day, Jan 9th!
Link to register:
http://introtodeeplearning.com
MIT Introduction to Deep Learning The 2022 lectures can be found here:
https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI
👉 @deeplearning_ai
🔥 Machine Learning Operations (MLOps) Specialization Course Demo
# FREE CLASS
Learn to Design production-ready ML Pipelines to Build, Train and Deploy your Machine learning models on AWS, Azure, GCP & Open- Source tools
📈 Key Highlights of course
✔️ 40 Hours of Live sessions from Industrial Experts
✔️ 50+ Live Hands-on Labs
✔️ 5+ Real-time industrial projects
✔️ One-on-One with Industry Mentors
👉🏻 Registration Link
https://bit.ly/mlops-live
🧑🏻🎓 What You Will Learn?
▪️Introduction to ML and MLOps stages
▪️Introduction to Git & CI/CD
▪️Docker & Kubernetes Overview
▪️Kubernetes Deployment Strategy
▪️Introduction to Model Management
▪️Feature Store
▪️Cloud ML Services 101
▪️Kubeflow Intro
▪️Introduction to Model Monitoring
▪️Introduction to Automl tools
▪️Post-Deployment Challenges
☎️ Contact:
Sarath Kumar
+918940876397 / +918778033930
GPT-4 Technical Report
Source code: https://github.com/openai/evals
Paper: https://cdn.openai.com/papers/gpt-4.pdf
3D-aware Conditional Image Synthesis (pix2pix3D)
Pix2pix3D synthesizes 3D objects (neural fields) given a 2D label map, such as a segmentation or edge map
Github:
https://github.com/dunbar12138/pix2pix3D
Paper:
https://arxiv.org/abs/2302.08509
Project:
https://www.cs.cmu.edu/~pix2pix3D/
Datasets:
CelebAMask , AFHQ-Cat-Seg , Shapenet-Car-Edge
@deeplearning_ai
Machine Learning Operations (MLOps) Masterclass
🏆 Unlock your full potential with MLOps Masterclass
Learn to Design ML Pipelines to Build, Train,Deploy and Monitor your Machine learning models in a real-time production environment.
Register Now👇
https://bit.ly/mlops-class
Why you shouldn't miss this Masterclass?
✔️ 15+ hands-on exercises.
✔️ 2 Real-life industry projects.
✔️Dedicated mentoring sessions from industry experts.
✔️ 10 hours session consisting of theory + Hands-on.
Schedule:
11th,Sat & 12th,Sun March
Highlights of this Masterclass:
▪️Machine Learning Operations (MLOps) Introduction
▪️Getting started with AWS for Machine Learning
▪️AWS SageMaker
▪️CI/CD Tools
▪️AWS MLOps Tools
▪️AWS MLOps - Build, Train & deploy ML Model
DiffusionInst: Diffusion Model for Instance Segmentation
* DiffusionInst is the first work of diffusion model for instance segmentation
Github:
https://github.com/chenhaoxing/DiffusionInst
Paper:
https://arxiv.org/abs/2212.02773v2
Getting started:
https://github.com/chenhaoxing/DiffusionInst/blob/main/GETTING_STARTED.md
Dataset:
https://paperswithcode.com/dataset/lvis
VTOONIFY: CONTROLLABLE HIGH-RESOLUTION PORTRAIT VIDEO STYLE TRANSFER
Project page: https://www.mmlab-ntu.com/project/vtoonify/
G.COLAB: https://colab.research.google.com/github/williamyang1991/VToonify/blob/master/notebooks/inference_playground.ipynb
source code: https://github.com/williamyang1991/vtoonify
Paper: VToonify: Controllable High-Resolution Portrait Video Style Transfer
Accurate and Efficient Stereo Matching via Attention Concatenation Volume
Stereo Depth Estimation
Paper:
https://arxiv.org/pdf/2209.12699.pdf
Github:
https://github.com/gangweiX/Fast-ACVNet
Demo:
https://www.youtube.com/watch?v=az4Z3dp72Zw
ONNX:
ONNX-FastACVNet-Stereo-Depth-Estimation
Access to high-paying remote web3 jobs: https://t.me/web3hiring
Web3 networking & discussion group: https://t.me/hashtagweb3
YOLOv8 is the newest state-of-the-art YOLO model that can be used for object detection, image classification, and instance segmentation tasks. YOLOv8 includes numerous architectural and developer experience changes and improvements over YOLOv5.
Code:
https://github.com/ultralytics/ultralytics
What's New in YOLOv8 ?
https://blog.roboflow.com/whats-new-in-yolov8/
Yolov8 Instance Segmentation (ONNX):
https://github.com/ibaiGorordo/ONNX-YOLOv8-Instance-Segmentation
