Unifying Flow, Stereo and Depth Estimation

Project Page: https://haofeixu.github.io/unimatch/

PAPER: https://arxiv.org/abs/2211.05783

Colab : https://colab.research.google.com/drive/1r5m-xVy3Kw60U-m5VB-aQ98oqqg_6cab?usp=sharing

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GALACTICA is a general-purpose scientific language model. It is trained on a large corpus of scientific text and data. It can perform scientific NLP tasks at a high level, as well as tasks such as citation prediction, mathematical reasoning, molecular property prediction and protein annotation. More information is available at galactica.org.

PAPER: https://arxiv.org/pdf/2211.09085v1.pdf
SOURCE CODE: https://github.com/paperswithcode/galai

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Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

Paper:
https://arxiv.org/pdf/2207.10660.pdf

Github:
https://github.com/facebookresearch/omni3d

Project page:
https://garrickbrazil.com/omni3d/

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Learning Video Representations from Large Language Models

Paper:
https://arxiv.org/abs/2212.04501

Github:
https://github.com/facebookresearch/lavila

Colab:
https://huggingface.co/spaces/nateraw/lavila

Project page:
https://facebookresearch.github.io/LaViLa/

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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-demo-course

🧑🏻‍🎓 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

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

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@Deeplearning_ai

Welcome to the Ultralytics YOLOv8 🚀 notebook! YOLOv8 is the latest version of the YOLO object detection and image segmentation model developed by Ultralytics.
The YOLOv8 models are designed to be fast, accurate, and easy to use, making them an excellent choice for a wide range of object detection and image segmentation tasks.

source code: https://github.com/ultralytics/ultralytics

colab : https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb#scrollTo=t6MPjfT5NrKQ

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.

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

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

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Access to high-paying remote web3 jobs: https://hottg.com/web3hiring

Web3 networking & discussion group: https://hottg.com/hashtagweb3

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


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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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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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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.

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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

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

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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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