Now removing, duplicating or enhancing objects in video is more realistic with the assist of AI

"We need to talk about the car in the room."
This paper: what car? 🙈

Paper:
https://arxiv.org/pdf/2108.11250v1.pdf

Github:
https://github.com/hustvl/YOLOP

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

👉@computer_science_and_programming

PASS: Pictures without humAns for Self-Supervised Pretraining

PASS is a large-scale image dataset that does not include any humans, human parts, or other personally identifiable information

Github
https://github.com/yukimasano/PASS

Paper
https://arxiv.org/abs/2109.13228v1

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

Documentation
https://www.robots.ox.ac.uk/~vgg/research/pass/

8-bit optimizers – a replacement for regular optimizers. 🚀, 75% less memory, same with upwards trend, no hyperparam tuning needed Input symbol for numbers: #Lightweight, #LessMemory

Under review as a conference paper at ICLR 2022

8-BIT OPTIMIZERS VIA BLOCK-WISE QUANTIZATION

Paper:
https://arxiv.org/abs/2110.02861

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

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

Documentation:
https://bitsandbytes.readthedocs.io/en/latest/

👉@computer_science_and_programming

One of the best reference book is definately "Deep Learning with Python" (1st edition) by François Chollet (creator of Keras)

Deep Learning with Python (2nd edition) has been released with 500 pages of code examples, theory, context, practical tips...

Book:
https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras

For online reading:
https://livebook.manning.com/book/deep-learning-with-python-second-edition/chapter-1/

Jupyter notebooks on Github:
https://github.com/fchollet/deep-learning-with-python-notebooks

👉👉@computer_science_and_programming

ESPnet: end-to-end text-to-speech processing toolkit

ESPnet2-TTS: Extending the Edge of TTS Research

Github: https://github.com/espnet/espnet

Docs: https://espnet.github.io/espnet/

Paper: https://arxiv.org/abs/2110.07840v1

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

Another state-of-the-art archtecture for Vision tasks:

Github: https://github.com/sail-sg/poolformer

Paper: https://arxiv.org/abs/2111.11418

Datasets: ImageNet, COCO, Ade20k

Colab: https://colab.research.google.com/github/sail-sg/poolformer/blob/main/misc/poolformer_demo.ipynb

👉👉@computer_science_and_programming

Object-aware cropping, a simple, fast and highly effective data augmentation alternative to random scene cropping for SELF-SUPERVISED LEARNING

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

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

👉👉@computer_science_and_programming

Happy new year
Thank you for being with us
We appreciate your patience to science and always try to provide best content for subscribers

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

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

http://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/

@computer_science_and_programming