Artificial Intelligence && Deep Learning
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Channel for who have a passion for -
* Artificial Intelligence
* Machine Learning
* Deep Learning
* Data Science
* Computer vision
* Image Processing
* Research Papers

With advertising offers contact: @ai_adminn
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Dive into Deep Learning

Interactive deep learning book with code, math, and discussions

Implemented with NumPy/MXNet, PyTorch, and TensorFlow

Adopted at 300 universities from 55 countries

@deeplearning_ai
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Papers with Code 2021 : A Year in Review.

Papers with Code indexes various machine learning artifacts β€” papers, code, results β€” to facilitate discovery and comparison. Using this data we can get a sense of what the ML community found useful and interesting this year. Below we summarize the top trending papers, libraries and datasets for 2021 on Papers with Code.

https://medium.com/paperswithcode/papers-with-code-2021-a-year-in-review-de75d5a77b8b

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β€”β€”β€”β€”β€”β€” ConvNeXt β€”β€”β€”β€”β€”β€”--


Facebook propose ConvNeXt, a pure ConvNet model constructed entirely from standard ConvNet modules. ConvNeXt is accurate, efficient, scalable and very simple in design.

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

Paper: https://arxiv.org/abs/2201.03545

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@deeplearning_ai
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#β€”β€”β€”β€”β€”CVPR_2021β€”β€”β€”β€”β€”


RefineMask: Towards High-Quality Instance Segmentation
with Fine-Grained Features (CVPR 2021)

[paper]
: download paper and enjoy

source: use source code and get awesome result

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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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t.me/MachineLearning_Programming

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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.

⭐ Code: https://github.com/Dana-Farber-AIOS/pathml
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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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@Deeplearning_ai
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Realtime Object-aware Monocular Depth Estimation in Onboard Systems

Video Paper Bibtex

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@Deeplearning_ai
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FLAG: Flow-based 3D Avatar Generation
from Sparse Observations
.

𝐇𝐒𝐠𝐑π₯𝐒𝐠𝐑𝐭𝐬:
βœ…FLow-based Avatar Generative
βœ…Conditional distro of body pose
βœ…Exact pose likelihood process
βœ…Invertibility -> oracle latent code

[PAPER] [Project Page]


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@Deeplearning_ai
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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
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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 (TΓΌbingen)

https://github.com/dair-ai/ML-YouTube-Courses

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@Deeplearning_ai
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