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

GPT-4 Technical Report

Source code: https://github.com/openai/evals

Paper: https://cdn.openai.com/papers/gpt-4.pdf

@deeplearning_ai

🔥 Machine Learning Operations (MLOps) Specialization Course Demo

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▪️Kubeflow Intro
▪️Introduction to Model Monitoring
▪️Introduction to Automl tools
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☎️ Contact:
Sarath Kumar
+918940876397 / +918778033930

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

"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

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

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

Course Schedule and Materials:
https://cs330.stanford.edu/

GET Free Course Link

Join us: @deeplarning_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-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)

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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

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

📢 FREE TRAINING: Master MLOps for ML deployment and operations! 🚀

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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

80+ Jupyter Notebook tutorials on image classification, object detection and image segmentation in various domains
📌 Agriculture and Food
📌 Medical and Healthcare
📌 Satellite
📌 Security and Surveillance
📌 ADAS and Self Driving Cars
📌 Retail and E-Commerce
📌 Wildlife

Classification library
https://github.com/Tessellate-Imaging/monk_v1

Notebooks - https://github.com/Tessellate-Imaging/monk_v1/tree/master/study_roadmaps/4_image_classification_zoo

Detection and Segmentation Library
https://github.com/Tessellate-Imaging/

Monk_Object_Detection
Notebooks: https://github.com/Tessellate-Imaging/Monk_Object_Detection/tree/master/application_model_zoo

👉 @deeplearning_ai

MLOps Masterclass

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🏆 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.

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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)

Schedule:
August 12th (Sat) & 13th (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 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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📢 FREE TRAINING: Master MLOps for ML deployment and operations! 🚀

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🌟 What you'll gain:
1️⃣ ML model deployment techniques.
2️⃣ Efficient data management insights.
3️⃣ Explore latest MLOps tools.
4️⃣ Real-time interaction with expert instructors.

🚩 Limited spots available! Don't miss out!

👉 Enroll now: https://bit.ly/mlops-free-training

👥 Share with fellow ML enthusiasts! 🚀✨