The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.
https://www.ritchieng.com/the-incredible-pytorch/
https://github.com/ritchieng/the-incredible-pytorch
@deeplearning_ai
https://www.ritchieng.com/the-incredible-pytorch/
https://github.com/ritchieng/the-incredible-pytorch
@deeplearning_ai
GitHub
GitHub - ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list of tutorials, papers, projects, communities andโฆ
The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch. - GitHub - ritchieng/the-incredible-pytorch: The Incredible PyTorch: a curated list...
321 Open Source Pytorch Implementation Software Projects
Free and open source pytorch implementation code projects including engines, APIs, generators, and tools.
https://opensourcelibs.com/libs/pytorch-implementation
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.
Free and open source pytorch implementation code projects including engines, APIs, generators, and tools.
https://opensourcelibs.com/libs/pytorch-implementation
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.
Want to jump ahead in artificial intelligence and/or digital pathology? Excited to share that after 2+ years of development PathML 2.0 is out! An open source #computational #pathology software library created by Dana-Farber Cancer Institute/Harvard Medical School and Weill Cornell Medicine led by Massimo Loda to lower the barrier to entry to #digitalpathology and #artificialintelligence , and streamline all #imageanalysis or #deeplearning workflows.
โญ Code: https://github.com/Dana-Farber-AIOS/pathml
โญ Code: https://github.com/Dana-Farber-AIOS/pathml
GitHub
GitHub - Dana-Farber-AIOS/pathml: Tools for computational pathology
Tools for computational pathology. Contribute to Dana-Farber-AIOS/pathml development by creating an account on GitHub.
Image Super Resolution - PyImageSearch
https://pyimagesearch.com/2022/02/14/image-super-resolution/
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.
https://pyimagesearch.com/2022/02/14/image-super-resolution/
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.
PyImageSearch
Image Super Resolution - PyImageSearch
Understand and apply image super resolution in your work today. Free tutorial and complete code included.
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Multi Task Learning for 3D segmentation
Perception stack of an Autonomous Driving system often contains multiple neural networks working together to predict bounding boxes, segmentation maps, depth maps, lane lines etc. Having a separate neural network for each task creates an heavy impact on system's processing speed.
https://github.com/adithyagaurav/Multi_Task_Learning
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Perception stack of an Autonomous Driving system often contains multiple neural networks working together to predict bounding boxes, segmentation maps, depth maps, lane lines etc. Having a separate neural network for each task creates an heavy impact on system's processing speed.
https://github.com/adithyagaurav/Multi_Task_Learning
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Realtime Object-aware Monocular Depth Estimation in Onboard Systems
Video Paper Bibtex
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Video Paper Bibtex
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FLAG: Flow-based 3D Avatar Generation
from Sparse Observations.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ FLow-based Avatar Generative
โ Conditional distro of body pose
โ Exact pose likelihood process
โ Invertibility -> oracle latent code
[PAPER] [Project Page]
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from Sparse Observations.
๐๐ข๐ ๐ก๐ฅ๐ข๐ ๐ก๐ญ๐ฌ:
โ FLow-based Avatar Generative
โ Conditional distro of body pose
โ Exact pose likelihood process
โ Invertibility -> oracle latent code
[PAPER] [Project Page]
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@Deeplearning_ai
If you are learning Machine Learning and wants to make end-to-end Machine Learning real-world projects, then this website can be a great resource for you.
It has project bundle(Dragon bundle) comprising more than 550+ real-world projects in ML, DL, DS, CV and NLP and PYTHON3.
More details are showned in the image above.
- Each project comes with required Dataset, complete source code(Python3) and documentation along with explanatory comments so that even beginner can understand.
- Life time access and projects are getting updates each month.
You can download the list of complete 550+ projects from our website.
Visit our website for more information.
Website Link:
https://tensorprojects.com/dragonbundle
It has project bundle(Dragon bundle) comprising more than 550+ real-world projects in ML, DL, DS, CV and NLP and PYTHON3.
More details are showned in the image above.
- Each project comes with required Dataset, complete source code(Python3) and documentation along with explanatory comments so that even beginner can understand.
- Life time access and projects are getting updates each month.
You can download the list of complete 550+ projects from our website.
Visit our website for more information.
Website Link:
https://tensorprojects.com/dragonbundle
At DAIR.AI we heart open education. We are excited to share some of the best and most recent machine learning courses available on YouTube.
Hot topics:
1. Stanford CS229: Machine Learning
2. Practical Deep Learning for Coders (2020)
3. Deep Unsupervised Learning
4. Advanced NLP
5. Deep Learning for Computer Vision
6. Deep Reinforcement Learning
7. Full Stack Deep Learning
8. Self-Driving Cars (Tรผbingen)
https://github.com/dair-ai/ML-YouTube-Courses
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@Deeplearning_ai
Hot topics:
1. Stanford CS229: Machine Learning
2. Practical Deep Learning for Coders (2020)
3. Deep Unsupervised Learning
4. Advanced NLP
5. Deep Learning for Computer Vision
6. Deep Reinforcement Learning
7. Full Stack Deep Learning
8. Self-Driving Cars (Tรผbingen)
https://github.com/dair-ai/ML-YouTube-Courses
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[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral)
https://github.com/Garfield-kh/PoseTriplet
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https://github.com/Garfield-kh/PoseTriplet
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GitHub
GitHub - Garfield-kh/PoseTriplet: [CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination underโฆ
[CVPR 2022] PoseTriplet: Co-evolving 3D Human Pose Estimation, Imitation, and Hallucination under Self-supervision (Oral) - Garfield-kh/PoseTriplet
Accelerate AI training in a few lines of code without changing the training setup.
https://github.com/nebuly-ai/nebulgym
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https://github.com/nebuly-ai/nebulgym
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GitHub
GitHub - nebuly-ai/nos: Module to Automatically maximize the utilization of GPU resources in a Kubernetes cluster through realโฆ
Module to Automatically maximize the utilization of GPU resources in a Kubernetes cluster through real-time dynamic partitioning and elastic quotas - Effortless optimization at its finest! - nebuly...
Kickstart your developer journey with free courses and cash rewards for you time in one place ๐๐ฐ
Use code "aideeplearning" to gain access: https://bit.ly/3KU21bq ๐
StackUp is a platform for devs where you can learn programming & Web3.0 technologies, all while earning. Get rewarded for completing a variety of learning quests.
With new campaigns each week, you can earn from a pool of over 10 000USD in cash rewards each month. This month's campaign has a total pool of 30 000USD worth of rewards! ๐
Use code "aideeplearning" to gain access: https://bit.ly/3KU21bq ๐
StackUp is a platform for devs where you can learn programming & Web3.0 technologies, all while earning. Get rewarded for completing a variety of learning quests.
With new campaigns each week, you can earn from a pool of over 10 000USD in cash rewards each month. This month's campaign has a total pool of 30 000USD worth of rewards! ๐
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EG3D: Efficient Geometry-aware 3D Generative Adversarial Networks
Official PyTorch implementation of the CVPR 2022 paper.
Project page: https://nvlabs.github.io/eg3d/
Paper: https://arxiv.org/abs/2112.07945
Source code: https://github.com/NVlabs/eg3d
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Official PyTorch implementation of the CVPR 2022 paper.
Project page: https://nvlabs.github.io/eg3d/
Paper: https://arxiv.org/abs/2112.07945
Source code: https://github.com/NVlabs/eg3d
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@Deeplearning_ai
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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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 โ 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
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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๐ Welcome to
@realgroupforprogrammer ๐
๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐จโ๐ป
๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐ต๐ถ๐ฐ๐ฎ๐น ๐๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด ๐
๐๐ฒ๐ฎ๐ฟ๐ป ๐๐น๐ฎ๐ฐ๐ธ๐๐ฎ๐ ๐ ๐ฒ๐๐ต๐ผ๐ฑ๐ ๐
๐๐ป๐ฑ ๐บ๐๐ฐ๐ต ๐บ๐ผ๐ฟ๐ฒ ๐น๐ฎ๐๐ฒ๐๐ ๐๐ฒ๐ฐ๐ต๐ป๐ถ๐ฐ๐ฎ๐น ๐บ๐ฒ๐๐ต๐ผ๐ฑ๐, ๐๐ถ๐ฝ๐ ๐ฎ๐ป๐ฑ ๐๐ฟ๐ถ๐ฐ๐ธ๐.
๐ป ๐๐ฒ๐ฟ๐ฒ ๐๐ผ๐ ๐ฐ๐ฎ๐ป ๐น๐ฒ๐ฎ๐ฟ๐ป :- ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด, ๐๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด, ๐๐ฟ๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด, ๐ช๐ฒ๐ฏ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐, ๐๐ฝ๐ฝ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐, ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ถ๐ฎ๐น ๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ, ๐๐ฒ๐ฒ๐ฝ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ, ๐๐ถ๐ด๐ถ๐๐ฎ๐น ๐ ๐ฎ๐ฟ๐ธ๐ฒ๐๐ถ๐ป๐ด, ๐๐ฟ๐ฎ๐ฝ๐ต๐ถ๐ฐ ๐ฑ๐ฒ๐๐ถ๐ด๐ป, ๐๐ป๐ถ๐บ๐ฎ๐๐ถ๐ผ๐ป, ๐ฉ๐ถ๐ฑ๐ฒ๐ผ ๐ฒ๐ฑ๐ถ๐๐ถ๐ป๐ด, ๐ฃ๐ต๐ผ๐๐ผ๐ด๐ฟ๐ฎ๐ฝ๐ต๐, ๐ฃ๐ต๐ผ๐๐ผ๐ ๐ฒ๐ฑ๐ถ๐๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐บ๐ฎ๐ป๐ ๐บ๐ผ๐ฟ๐ฒ ๐น๐ผ๐๐ ๐ผ๐ณ ๐๐ต๐ถ๐ป๐ด ๐ถ๐ป ๐ณ๐ฟ๐ฒ๐ฒ ๐๐ ๐
โ ๐ ๐ฐ๐น๐ฒ๐ฎ๐ป ๐น๐ถ๐ฏ๐ฟ๐ฎ๐ฟ๐ ๐ณ๐ผ๐ฟ ๐ด๐ฒ๐ฒ๐ธ๐.
๐๐ฒ๐ ๐๐๐ด ๐๐ผ๐๐ป๐๐, ๐ก๐ฒ๐๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด, ๐๐๐ต๐ถ๐ฐ๐ฎ๐น ๐๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด, ๐๐๐ฏ๐ฒ๐ฟ๐๐ฒ๐ฐ๐๐ฟ๐ถ๐๐, ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด & ๐น๐ผ๐ ๐บ๐ผ๐ฟ๐ฒ ๐น๐ฎ๐๐ฒ๐๐ ๐๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ ๐ฏ๐ฎ๐๐ฒ๐ฑ ๐ฒ๐๐ผ๐ผ๐ธ๐.
๐๐ป ๐๐ต๐ถ๐ ๐๐ต๐ฎ๐ป๐ป๐ฒ๐น, ๐ฌ๐ผ๐ ๐๐ถ๐น๐น ๐ด๐ฒ๐ ๐จ๐ฑ๐ฒ๐บ๐ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐, ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ฟ๐ฎ ๐๐ผ๐๐ฟ๐๐ฒ๐, & ๐๐ฟ๐ฒ๐ฒ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐.
๐๐ค๐ง ๐๐ง๐๐ ๐๐ค๐ช๐ง๐จ๐๐จ,๐๐ค๐ค๐ ๐จ,๐ฅ๐ง๐ค๐๐๐๐ฉ๐จ,๐๐ฃ๐ฉ๐๐ง๐ฃ๐จ๐๐๐ฅ๐จ,๐ฅ๐ก๐๐๐๐ข๐๐ฃ๐ฉ๐จ ๐๐ฃ๐ ๐๐ค๐๐จ ๐ง๐๐ก๐๐ฉ๐๐ ๐ข๐๐ฉ๐๐ง๐๐๐ก๐จ ๐๐ฃ๐ ๐ช๐ฅ๐๐๐ฉ๐๐จ ๐๐ค๐๐ฃ ๐ค๐ช๐ง ๐ฉ๐๐ก๐๐๐ง๐๐ข ๐๐๐๐ฃ๐ฃ๐๐ก:
https://hottg.com/realgroupforprogrammer
๐ฆ๐ผ ๐๐ต๐ฎ๐ ๐ฎ๐ฟ๐ฒ ๐๐ผ๐ ๐๐ฎ๐ถ๐๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ?
๐๐ผ๐ถ๐ป ๐ฟ๐ถ๐ด๐ต๐ ๐ป๐ผ๐๐
https://hottg.com/realgroupforprogrammer
@realgroupforprogrammer ๐
๐๐ฒ๐ฎ๐ฟ๐ป ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐จโ๐ป
๐๐ฒ๐ฎ๐ฟ๐ป ๐๐๐ต๐ถ๐ฐ๐ฎ๐น ๐๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด ๐
๐๐ฒ๐ฎ๐ฟ๐ป ๐๐น๐ฎ๐ฐ๐ธ๐๐ฎ๐ ๐ ๐ฒ๐๐ต๐ผ๐ฑ๐ ๐
๐๐ป๐ฑ ๐บ๐๐ฐ๐ต ๐บ๐ผ๐ฟ๐ฒ ๐น๐ฎ๐๐ฒ๐๐ ๐๐ฒ๐ฐ๐ต๐ป๐ถ๐ฐ๐ฎ๐น ๐บ๐ฒ๐๐ต๐ผ๐ฑ๐, ๐๐ถ๐ฝ๐ ๐ฎ๐ป๐ฑ ๐๐ฟ๐ถ๐ฐ๐ธ๐.
๐ป ๐๐ฒ๐ฟ๐ฒ ๐๐ผ๐ ๐ฐ๐ฎ๐ป ๐น๐ฒ๐ฎ๐ฟ๐ป :- ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด, ๐๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด, ๐๐ฟ๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด, ๐ช๐ฒ๐ฏ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐, ๐๐ฝ๐ฝ ๐ฑ๐ฒ๐๐ฒ๐น๐ผ๐ฝ๐บ๐ฒ๐ป๐, ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐๐ฟ๐๐ถ๐ณ๐ถ๐ฐ๐ถ๐ฎ๐น ๐๐ป๐๐ฒ๐น๐น๐ถ๐ด๐ฒ๐ป๐ฐ๐ฒ, ๐๐ฒ๐ฒ๐ฝ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด, ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ, ๐๐ถ๐ด๐ถ๐๐ฎ๐น ๐ ๐ฎ๐ฟ๐ธ๐ฒ๐๐ถ๐ป๐ด, ๐๐ฟ๐ฎ๐ฝ๐ต๐ถ๐ฐ ๐ฑ๐ฒ๐๐ถ๐ด๐ป, ๐๐ป๐ถ๐บ๐ฎ๐๐ถ๐ผ๐ป, ๐ฉ๐ถ๐ฑ๐ฒ๐ผ ๐ฒ๐ฑ๐ถ๐๐ถ๐ป๐ด, ๐ฃ๐ต๐ผ๐๐ผ๐ด๐ฟ๐ฎ๐ฝ๐ต๐, ๐ฃ๐ต๐ผ๐๐ผ๐ ๐ฒ๐ฑ๐ถ๐๐ถ๐ป๐ด ๐ฎ๐ป๐ฑ ๐บ๐ฎ๐ป๐ ๐บ๐ผ๐ฟ๐ฒ ๐น๐ผ๐๐ ๐ผ๐ณ ๐๐ต๐ถ๐ป๐ด ๐ถ๐ป ๐ณ๐ฟ๐ฒ๐ฒ ๐๐ ๐
โ ๐ ๐ฐ๐น๐ฒ๐ฎ๐ป ๐น๐ถ๐ฏ๐ฟ๐ฎ๐ฟ๐ ๐ณ๐ผ๐ฟ ๐ด๐ฒ๐ฒ๐ธ๐.
๐๐ฒ๐ ๐๐๐ด ๐๐ผ๐๐ป๐๐, ๐ก๐ฒ๐๐๐ผ๐ฟ๐ธ๐ถ๐ป๐ด, ๐๐๐ต๐ถ๐ฐ๐ฎ๐น ๐๐ฎ๐ฐ๐ธ๐ถ๐ป๐ด, ๐๐๐ฏ๐ฒ๐ฟ๐๐ฒ๐ฐ๐๐ฟ๐ถ๐๐, ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด & ๐น๐ผ๐ ๐บ๐ผ๐ฟ๐ฒ ๐น๐ฎ๐๐ฒ๐๐ ๐๐ฒ๐ฐ๐ต๐ป๐ผ๐น๐ผ๐ด๐ ๐ฏ๐ฎ๐๐ฒ๐ฑ ๐ฒ๐๐ผ๐ผ๐ธ๐.
๐๐ป ๐๐ต๐ถ๐ ๐๐ต๐ฎ๐ป๐ป๐ฒ๐น, ๐ฌ๐ผ๐ ๐๐ถ๐น๐น ๐ด๐ฒ๐ ๐จ๐ฑ๐ฒ๐บ๐ ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐, ๐๐ฟ๐ฒ๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐ฟ๐ฎ ๐๐ผ๐๐ฟ๐๐ฒ๐, & ๐๐ฟ๐ฒ๐ฒ๐ข๐ป๐น๐ถ๐ป๐ฒ ๐๐ผ๐๐ฟ๐๐ฒ๐.
๐๐ค๐ง ๐๐ง๐๐ ๐๐ค๐ช๐ง๐จ๐๐จ,๐๐ค๐ค๐ ๐จ,๐ฅ๐ง๐ค๐๐๐๐ฉ๐จ,๐๐ฃ๐ฉ๐๐ง๐ฃ๐จ๐๐๐ฅ๐จ,๐ฅ๐ก๐๐๐๐ข๐๐ฃ๐ฉ๐จ ๐๐ฃ๐ ๐๐ค๐๐จ ๐ง๐๐ก๐๐ฉ๐๐ ๐ข๐๐ฉ๐๐ง๐๐๐ก๐จ ๐๐ฃ๐ ๐ช๐ฅ๐๐๐ฉ๐๐จ ๐๐ค๐๐ฃ ๐ค๐ช๐ง ๐ฉ๐๐ก๐๐๐ง๐๐ข ๐๐๐๐ฃ๐ฃ๐๐ก:
https://hottg.com/realgroupforprogrammer
๐ฆ๐ผ ๐๐ต๐ฎ๐ ๐ฎ๐ฟ๐ฒ ๐๐ผ๐ ๐๐ฎ๐ถ๐๐ถ๐ป๐ด ๐ณ๐ผ๐ฟ?
๐๐ผ๐ถ๐ป ๐ฟ๐ถ๐ด๐ต๐ ๐ป๐ผ๐๐
https://hottg.com/realgroupforprogrammer
Telegram
GROUP FOR PROGRAMMERS๐ฅ
Here we will provide premium free online courses, projects, trainings, internships, placements and jobs related material and updates.
Join discussion group: @groupforprogrammerofficial
admin: @tcpl0
Join discussion group: @groupforprogrammerofficial
admin: @tcpl0
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EasyMocap is an open-source toolbox for markerless human motion capture and novel view synthesis from RGB videos. In this project, we provide a lot of motion capture demos in different settings.
source code: https://github.com/zju3dv/EasyMocap
Paper : https://dl.acm.org/doi/abs/10.1145/3528233.3530704
Colab: https://colab.research.google.com/drive/1Cyvu_lPFUajr2RKt6yJIfS3HQIIYl6QU?usp=sharing
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source code: https://github.com/zju3dv/EasyMocap
Paper : https://dl.acm.org/doi/abs/10.1145/3528233.3530704
Colab: https://colab.research.google.com/drive/1Cyvu_lPFUajr2RKt6yJIfS3HQIIYl6QU?usp=sharing
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