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:

India6 023 Tecnologías y Aplicaciones2 297...

📈 Análisis del canal de Telegram Artificial Intelligence && Deep Learning

El canal Artificial Intelligence && Deep Learning (@deeplearning_ai) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 58 024 suscriptores, ocupando la posición 2 297 en la categoría Tecnologías y Aplicaciones y el puesto 6 023 en la región India.

📊 Métricas de audiencia y dinámica

Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 58 024 suscriptores.

Según los últimos datos del 23 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -218, y en las últimas 24 horas de -10, conservando un alto alcance.

Estado de verificación: No verificado
Tasa de interacción (ER): El promedio de interacción de la audiencia es 8.90%. Durante las primeras 24 horas tras publicar, el contenido suele obtener N/A% de reacciones respecto al total de suscriptores.
Alcance de las publicaciones: Cada publicación recibe en promedio 5 163 visualizaciones. En el primer día suele acumular 0 visualizaciones.
Reacciones e interacción: La audiencia responde de forma activa: el promedio de reacciones por publicación es 15.
Intereses temáticos: El contenido se centra en temas clave como github, learning, estimation, dataset, engineer.

📝 Descripción y política de contenido

El autor describe el recurso como un espacio para expresar opiniones subjetivas:
“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:”

Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 24 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.

58 024

Suscriptores

-1024 horas

-557 días

-21830 días

5 163

Visitas de la publicación

Sin datos24 horas

Sin datos48 horas

8.90%

Tasa de compromiso

Sin datos

Mensajes por día

Ads index

beta

Archivo de publicaciones

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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

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🚨 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!

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Source code: https://github.com/facebookresearch/imagebind Paper: https://arxiv.org/pdf/2305.05665v1.pdf @deeplearning_ai

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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

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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

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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

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reallyyyy amazing......

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"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

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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

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🔥 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

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GPT-4 Technical Report Source code: https://github.com/openai/evals Paper: https://cdn.openai.com/papers/gpt-4.pdf

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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

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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

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https://youtu.be/yIFCQGBrHls

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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

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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

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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

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Access to high-paying remote web3 jobs: https://t.me/web3hiring Web3 networking & discussion group: https://t.me/hashtagweb3

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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