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

MIT Introduction to Deep Learning - 2023 Starting soon! MIT Intro to DL is one of the most concise AI courses on the web that cover basic deep learning techniques, architectures, and applications.

2023 lectures are starting in just one day, Jan 9th!

Link to register:
https://introtodeeplearning.com

MIT Introduction to Deep Learning The 2022 lectures can be found here:

https://m.youtube.com/playlist?list=PLtBw6njQRU-rwp5__7C0oIVt26ZgjG9NI

https://t.iss.one/DataScienceT