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

✨ Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning

Github: https://github.com/sense-x/uniformer

Paper: https://arxiv.org/abs/2201.04676v1

Tasks: https://paperswithcode.com/dataset/kinetics-600

@computer_science_and_programming

323+ 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

A curated list of tutorials, papers, projects, communities and more related to PyTorch:

https://www.ritchieng.com/the-incredible-pytorch/

https://github.com/ritchieng/the-incredible-pytorch


@computer_science_and_programming

A lightweight vision library for performing large scale object detection & instance segmentation

Github: https://github.com/obss/sahi

Paper: https://arxiv.org/abs/2202.06934v1

Kaggle notebook: https://www.kaggle.com/remekkinas/sahi-slicing-aided-hyper-inference-yv5-and-yx

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

👉👉@computer_science_and_programming

💬 A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution

Github: https://github.com/mjq11302010044/tatt

Paper: https://arxiv.org/abs/2203.09388v2

Dataset: https://deepchecks.com/blog/

@computer_science_and_programming

NAFSSR: Stereo Image Super-Resolution Using NAFNet

Github: https://github.com/megvii-research/NAFNet

Paper: https://arxiv.org/abs/2204.08714v1

Demo: https://colab.research.google.com/drive/1dkO5AyktmBoWwxBwoKFUurIDn0m4qDXT?usp=sharing

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

@computer_science_and_programming

🧊 Focal Sparse Convolutional Networks for 3D Object Detection (CVPR 2022, Oral)

Github: https://github.com/dvlab-research/focalsconv

Paper: https://arxiv.org/abs/2204.12463

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

@computer_science_and_programming

AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition

Github: https://github.com/ShoufaChen/AdaptFormer

Paper: https://arxiv.org/abs/2205.13535v1

Dataset: https://paperswithcode.com/dataset/something-something-v2

@computer_science_and_programming