Lightweight Python library for adding real-time object tracking to any detector.

https://github.com/tryolabs/norfair


@deeplearning_ai

Accelerate AI training in a few lines of code without changing the training setup.


https://github.com/nebuly-ai/nebulgym



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@Deeplearning_ai

Free courses and quests in Coding & Web3.0 with cash rewards worth of 30 000USD 👇
https://bit.ly/3FpfqHr

Use code: "machinelearning0" when signing up to get access and start learning 🙌

PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction.

Paper: https://arxiv.org/abs/2007.03858

Project Page: http://www.liuyebin.com/pamir/pamir.html

Source code: https://github.com/ZhengZerong/PaMIR


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@MachineLearning_Programming

CVPR 2022 Open Access...


Open Access versions, provided by the Computer Vision Foundation.
Except for the watermark, they are identical to the accepted versions; the final published version of the proceedings is available on IEEE Xplore.

https://openaccess.thecvf.com/CVPR2022?day=2022-06-21



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@Deeplearning_ai

Harvard CS109A #DataScience course materials — huge collection free & open!

1. Lecture notes
2. R code, #Python notebooks
3. Lab material
4. Advanced sections
and more ...

https://harvard-iacs.github.io/2019-CS109A/pages/materials.html


It will be really useful for you


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@Deeplearning_ai

Pycharm keyboard shortcuts


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@Deeplearning_ai

You don't need to spend several $𝟭𝟬𝟬𝟬𝘀 to learn Data Science.❌

Stanford University, Harvard University & Massachusetts Institute of Technology is providing free courses.💥

Here's 8 free Courses that'll teach you better than the paid ones:


1. CS50’s Introduction to Artificial Intelligence with Python (Harvard)

https://lnkd.in/d9CkkfGK

2. Data Science: Machine Learning (Harvard)

https://lnkd.in/dQ7zkCv9

3. Artificial Intelligence (MIT)

https://lnkd.in/dG5BCPen

4. Introduction to Computational Thinking and Data Science (MIT)

https://lnkd.in/ddm5Ckk9

5. Machine Learning (MIT)

https://lnkd.in/dJEjStCw

6. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)

https://lnkd.in/dkpyt6qr

7. Statistical Learning (Stanford)

https://lnkd.in/dymn4hbD

8. Mining Massive Data Sets (Stanford)

📍https://lnkd.in/d2uf-FkB



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@MachineLearning_Programming

Deep Face Restoartion: Denoise, Super-Resolution, Deblur and Artifact Removal


Table of Contents
* Surveys
* Deep Blind Face Restoration
* Deep Face Super-Resolution
* Deep Face Deblurring
* Deep Face Denoising
* Deep Face Artifact Removal
* Other Related Works
* Image Quality Assessment
* Benchmark Datasets
* Recommended Datasets
* All Datasets

Paper: https://arxiv.org/pdf/2211.02831v1.pdf

source code: https://github.com/taowangzj/awesome-face-restoration

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@MachineLearning_Programming

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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@Deeplearning_ai

🔥 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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@MachineLearning_Programming

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

@MachineLearning_Programming

GLIGEN: Open-Set Grounded Text-to-Image Generation

GLIGEN (Grounded-Language-to-Image Generation) a novel approach that builds upon and extends the functionality of existing pre-trained text-to-image diffusion models by enabling them to also be conditioned on grounding inputs.

Project page:
https://gligen.github.io/

Paper:
https://arxiv.org/abs/2301.07093

Github (coming soon):
https://github.com/gligen/GLIGEN

Demo:
https://huggingface.co/spaces/gligen/demo

@MachineLearning_Programming

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

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


@MachineLearning_Programming