Computer Science and Programming

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Make your all writing and communications easy with Pismo writing assistant for Mac.

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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 - 🔗


JOIN: @MachineLearning_Programming

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

Computer Science and Programming pinned a photo

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join us: @MachineLearning_Programming

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

YouTube: https://youtube.com/playlist?list=PLoROMvodv4rOwvldxftJTmoR3kRcWkJBp

@deeplearning_ai

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Look what others ask from AI and Fork (continue) their conversation your way!

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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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1️⃣ ML model deployment techniques.
2️⃣ Efficient data management insights.
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4️⃣ Real-time interaction with expert instructors.

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👥 Share with fellow ML enthusiasts! 🚀✨

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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

Machine Learning Operations (MLOps) Masterclass

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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)

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This masterclass is perfect for Data scientists, ML engineers, Software engineers, and DevOps professionals.

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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