From Google and Waymo researchers: The self-/unsupervised revolution is near! Unsupervised optical flow model SMURF improves SOTA by 40% and beats many supervised methods such as PWC-Net and FlowNet2

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SMURF: Self-Teaching Multi-Frame Unsupervised RAFT with Full-Image Warping

Paper:
https://arxiv.org/pdf/2105.07014.pdf

Video:
https://www.youtube.com/watch?v=W7NCbfZp6QE

Code:
https://github.com/google-research/google-research/tree/master/smurf

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Paper:
https://arxiv.org/pdf/2105.06993.pdf

Project Page:
https://omnimatte.github.io/

Github:
https://github.com/erikalu/omnimatte


Supplimentary material:
https://omnimatte.github.io/supplementary/index.html

Explained:
https://www.youtube.com/watch?v=lCBSGOwV-_o

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Code:
https://github.com/gist-ailab/uoais#unseen-object-amodal-instance-segmentation-uoais

Paper:
https://arxiv.org/abs/2109.11103

Dataset:
https://paperswithcode.com/dataset/ocid

Project page:
https://sites.google.com/view/uoais


join us: @deeplearning_ai

An important collection of the 15 best machine learning cheat sheets.

مجموعة مهمة الافضل ١٥ ورقة غش في مجال التعلم الآلي.

1- Supervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf

2- Unsupervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf

3- Deep Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf

4- Machine Learning Tips and Tricks

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf

5- Probabilities and Statistics

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf

6- Comprehensive Stanford Master Cheat Sheet

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf

7- Linear Algebra and Calculus

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf

8- Data Science Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf

9- Keras Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf

10- Deep Learning with Keras Cheat Sheet

https://github.com/rstudio/cheatsheets/raw/master/keras.pdf

11- Visual Guide to Neural Network Infrastructures

https://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

12- Skicit-Learn Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

13- Scikit-learn Cheat Sheet: Choosing the Right Estimator

https://scikit-learn.org/stable/tutorial/machine_learning_map/

14- Tensorflow Cheat Sheet

https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf

15- Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

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Summary

Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.

What's Inside:
* Deep learning from first principles
* Setting up your own deep-learning environment
* Image-classification models
* Deep learning for text and sequences
* Neural style transfer, text generation, and image generation

@Deeplearning_aiDeep Learning with Python (2021)

Invite your friends 🌹🌹
@deeplearning_ai

Deep Learning with Python (2021)

Invite your friends 🌹🌹
@deeplearning_ai

Welcome to the Code Programmer community.

Our community offers many software projects with source code attached to explanations about the codes

In addition, we support both Arabic and English languages ​​at the same time.

https://t.iss.one/CodeProgrammer

Join the channel of researchers and programmers, the channel includes a huge encyclopedia of programming books and scientific articles in addition to the most famous scientific projects

t.iss.one/datascience_books

Review — DeepFace: Closing the Gap to Human-Level Performance in Face Verification

DeepFace for Face Verification After Face Alignment

https://sh-tsang.medium.com/review-deepface-closing-the-gap-to-human-level-performance-in-face-verification-973442ad7850


https://t.iss.one/DeepLearning_ai

NeurIPS 2021—10 papers you shouldn’t miss

2334 papers, 60 workshops, 8 keynote speakers, 15k+ attendees. A dense landscape that’s hard to navigate without a good guide and map, so here are some of our ideas!

https://towardsdatascience.com/neurips-2021-10-papers-you-shouldnt-miss-80f9c0793a3a

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@deeplearning_ai

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

Page: https://d2l.ai/

PyTorch based: https://d2l.ai/d2l-en-pytorch.pdf

MXNET based: https://d2l.ai/d2l-en.pdf

Github: https://github.com/d2l-ai/d2l-en

👉👉@deeplearning_ai

—————— ConvNeXt ——————--


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