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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/

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✨ 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

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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


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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

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💬 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/

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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

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🧊 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

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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

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Squeezeformer: An Efficient Transformer for Automatic Speech Recognition

Github: https://github.com/kssteven418/squeezeformer

Paper: https://arxiv.org/abs/2206.00888v1

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

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MIT, Introduction to Deep Learning, 2022 Lecture series

Website:
http://introtodeeplearning.com/

Lecture:
https://www.youtube.com/watch?v=7sB052Pz0sQ&list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

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Prosody Cloning in Zero-Shot Multispeaker Text-to-Speech

IMS Toucan is a toolkit for teaching, training and using state-of-the-art Speech Synthesis models.

Github: https://github.com/DigitalPhonetics/IMS-Toucan
https://github.com/rballester/tntorch

Pre-Generated Audios: https://multilingualtoucan.github.io/

Cloning prosody across speakers: https://toucanprosodycloningdemo.github.io/

Interactive Demo: https://huggingface.co/spaces/Flux9665/IMS-Toucan

Paper: https://arxiv.org/abs/2206.12229v1

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Instance Shadow Detection with A Single-Stage Detector

Deep framework, and an evaluation metric to approach this new task.

Github: https://github.com/stevewongv/InstanceShadowDetection

Instance Shadow Detection: https://github.com/stevewongv/SSIS

Video: https://www.youtube.com/watch?v=p0b_2SsFypw

Colab: https://colab.research.google.com/drive/1y9UpS5uA1YuoMyvYVzcKL4ltA_FDu_x0?usp=sharing

Paper: https://arxiv.org/abs/2207.04614v1

Datasets: https://paperswithcode.com/dataset/soba

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Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration

learnable parameter to dynamically adjust the semantic correlations and spatial context intensities for effective information propagation.

Github: https://github.com/164140757/scm

Paper: https://arxiv.org/abs/2207.10447v1

Dataset: https://paperswithcode.com/dataset/cub-200-2011

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UFO: segmentation 140+ FPS

👉Unified Transformer Framework for Co-Segmentation, Co-Saliency & Salient Object Detection. All in one!


𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:
✅Unified framework for co-segmentation
✅Co-segmentation, co-saliency, saliency
✅Block for long-range dependencies
✅Able to reach for 140 FPS in inference
✅The new SOTA on multiple datasets

Paper:
https://arxiv.org/pdf/2203.04708v2.pdf

Code:
https://github.com/suyukun666/UFO


@computer_science_and_programming

Harvard CS109A #DataScience course materials — 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

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