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Computer Science and Programming
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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 * Related Courses and Ebooks With advertising offers contact:
Mostrar más📈 Análisis del canal de Telegram Computer Science and Programming
El canal Computer Science and Programming (@machinelearning_programming) en el segmento lingüístico de Inglés es un actor destacado. Actualmente la comunidad reúne a 14 851 suscriptores, ocupando la posición 8 724 en la categoría Tecnologías y Aplicaciones y el puesto 29 599 en la región India.
📊 Métricas de audiencia y dinámica
Desde su creación el невідомо, el proyecto ha mostrado un crecimiento acelerado, reuniendo a 14 851 suscriptores.
Según los últimos datos del 03 junio, 2026, el canal mantiene una actividad estable. En los últimos 30 días la variación de miembros fue de -150, y en las últimas 24 horas de -3, conservando un alto alcance.
- Estado de verificación: No verificado
- Tasa de interacción (ER): El promedio de interacción de la audiencia es 14.63%. 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 0 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 0.
- Intereses temáticos: El contenido se centra en temas clave como learning, github, engineer, quantization, detection.
📝 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
* Related Courses and Ebooks
With advertising offers contact:”
Gracias a la alta frecuencia de actualizaciones (últimos datos recibidos el 04 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.
14 851
Suscriptores
-324 horas
-227 días
-15030 días
Archivo de publicaciones
+1NanoSAM is a Segment Anything (SAM) model variant that is capable of running in 🔥 real-time 🔥 on NVIDIA Jetson Orin Platforms with NVIDIA TensorRT.
source code:https://github.com/NVIDIA-AI-IOT/nanosam
Note: source code includes 👍 Usage - ⏱ Performance - 🛠 Setup - 🤸 Examples - 🏋️ Training
- 🧐 Evaluation - 👏 Acknowledgment - 🔗 See also
NanoSAM is a Segment Anything (SAM) model variant that is capable of running in 🔥 real-time 🔥 on NVIDIA Jetson Orin Platforms with NVIDIA TensorRT.
source code:https://github.com/NVIDIA-AI-IOT/nanosam
Note: source code includes 👍 Usage - ⏱ Performance - 🛠 Setup - 🤸 Examples - 🏋️ Training
- 🧐 Evaluation - 👏 Acknowledgment - 🔗 See also
join us: @MachineLearning_Programming
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Repost from Artificial Intelligence && Deep Learning
Stanford University has just opened full access to CS224U. One of their immensely popular graduate-level Natural Language Understanding course taught by Professor Christopher Potts.
GitHub: https://github.com/cgpotts/cs224u
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@deeplearning_ai
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Repost from Artificial Intelligence && Deep Learning
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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ImageBind: One Embedding Space To Bind Them All.
PyTorch implementation and pretrained models for ImageBind. For details, see the paper: ImageBind: One Embedding Space To Bind Them All.
ImageBind learns a joint embedding across six different modalities - images, text, audio, depth, thermal, and IMU data. It enables novel emergent applications ‘out-of-the-box’ including cross-modal retrieval, composing modalities with arithmetic, cross-modal detection and generation.
Source code: https://github.com/facebookresearch/imagebind
Paper: https://arxiv.org/pdf/2305.05665v1.pdf
@deeplearning_ai
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▪️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
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