Pandas for Data Science
Learning Path ⋅ Skills: Pandas, Data Science, Data Visualization

https://realpython.com/learning-paths/pandas-data-science/

https://t.iss.one/DataScienceT

Openicl

New open-source toolkit for ICL and LLM evaluation.

pip install openicl

🖥 Github: https://github.com/shark-nlp/openicl

⏩ Paper: https://arxiv.org/abs/2303.02913

⭐️ Dataset: https://paperswithcode.com/dataset/gsm8k

💨 Docs: https://github.com/shark-nlp/openicl#docs

⏩ Examples: https://github.com/Shark-NLP/OpenICL/tree/main/examples

https://t.iss.one/DataScienceT

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/

✳️ ساهم بنمو مجتمعنا من خلال اضافة الاصدقاء او مشاركة المنشور.

X-Avatar: Expressive Human Avatars

🖥 Github: https://github.com/Skype-line/X-Avatar

⏩ Paper: https://arxiv.org/abs/2303.04805

💨 Dataset: https://github.com/Skype-line/X-Avatar/blob/main/xxx

⏩ Project: https://skype-line.github.io/projects/X-Avatar/

https://t.iss.one/DataScienceT

Datasets

Datasets collected for network science, deep learning and general machine learning research.

Github: https://github.com/benedekrozemberczki/datasets

Paper: https://arxiv.org/abs/2101.03091v1

Invite your friends 🌹🌹
@DataScience_Books

Linear Algebra in Python: Matrix Inverses and Least Squares

https://realpython.com/python-linear-algebra/

GPT-4 Technical Report

Source code: https://github.com/openai/evals

Paper: https://cdn.openai.com/papers/gpt-4.pdf

Tuned Lens 🔎

Simple interface training and evaluating tuned lenses. A tuned lens allows us to peak at the iterative computations a transformer uses to compute the next token.

pip install tuned-lens

🖥 Github: https://github.com/alignmentresearch/tuned-lens

⏩ Paper: https://arxiv.org/abs/2303.08112v1

⭐️ Dataset: https://paperswithcode.com/dataset/the-pile

🖥 Colab: https://colab.research.google.com/github/AlignmentResearch/tuned-lens/blob/main/notebooks/interactive.ipynb

https://t.iss.one/DataScienceT

OpenSeeD

A Simple Framework for Open-Vocabulary Segmentation and Detection

🖥 Github: https://github.com/idea-research/openseed

⏩ Paper: https://arxiv.org/abs/2303.08131v2

💨 Dataset: https://paperswithcode.com/dataset/objects365

https://t.iss.one/DataScienceT

Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank

🖥 Github: https://github.com/huang-shirui/semi-uir

⏩ Paper: https://arxiv.org/abs/2303.09101v1

💨 Project: https://paperswithcode.com/dataset/uieb

https://t.iss.one/DataScienceT

WebSHAP: Towards Explaining Any Machine Learning Models Anywhere

🖥 Github: https://github.com/poloclub/webshap

⏩ Paper: https://arxiv.org/abs/2303.09545v1

💨 Project: https://poloclub.github.io/webshap

https://t.iss.one/DataScienceT

🖥 GigaGAN - Pytorch

Implementation of GigaGAN, new SOTA GAN out of Adobe.

https://github.com/lucidrains/gigagan-pytorch

https://t.iss.one/DataScienceT

Taming Diffusion Models for Audio-Driven Co-Speech Gesture Generation (CVPR 2023)

Novel Diffusion Audio-Gesture Transformer is devised to better attend to the information from multiple modalities and model the long-term temporal dependency.

🖥 Github: https://github.com/advocate99/diffgesture

⏩ Paper: https://arxiv.org/abs/2303.09119v1

💨 Dataset: https://paperswithcode.com/dataset/beat

https://t.iss.one/DataScienceT

Deep Metric Learning for Unsupervised CD

🖥 Github: https://github.com/wgcban/metric-cd

⏩ Paper: https://arxiv.org/abs/2303.09536v1

https://t.iss.one/DataScienceT

⚜️ ViperGPT: Visual Inference via Python Execution for Reasoning

ViperGPT, a framework that leverages code-generation models to compose vision-and-language models into subroutines to produce a result for any query.

🖥 Github: https://github.com/cvlab-columbia/viper

⏩ Paper: https://arxiv.org/pdf/2303.08128.pdf

💨 Project: https://paperswithcode.com/dataset/beat

https://t.iss.one/DataScienceT