sushil79g / Important_URL

List for best machine learning related things

Related to memory and computations:

Text searching and replacing: https://github.com/vi3k6i5/flashtext
Accelerated Dl and RL https://github.com/catalyst-team/catalyst
Distribute task: https://github.com/celery/celery
Numba: http://numba.pydata.org/

SOTA models:

NLP list: https://github.com/sebastianruder/NLP-progress
SOTA Pre-trained models: https://modelzoo.co/
Pytorch Hub and Tensorflow Hub

Torrent sites:

http://p30download.com/
http://academictorrents.com/

Learning resources:

A curated list of every free learning resources in Internet related to AI:
Link: https://airtable.com/shrSyz0zaGM52kksj/tbli6XwcjDSByLwLL/viw8FIuR24ToQ47Od?blocks=hide&fbclid=IwAR19eZEXs0sAByNjcg-y3Lnke59XCXMVV2mHVScFdjX5jfwIINO5yVnR8zA
A curated list of tutorials, papers, projects, communities and more relating to PyTorch.
Link:https://github.com/ritchieng/the-incredible-pytorch
Deep learning: https://github.com/kmario23/deep-learning-drizzle
Paper with code{mostly state of the art models}: https://paperswithcode.com/

Python libraries:

Widely used task-specific python libraries: https://github.com/vinta/awesome-python

Roadmap to be a data scientist:

https://trello.com/b/rbpEfMld/data-science

A curated list of libraries{Machine learning}:

https://github.com/josephmisiti/awesome-machine-learning#python
Pytorch: https://github.com/bharathgs/Awesome-pytorch-list

Library for neural architecture search, automl, model compression, hyper parameter tuning

NNI: https://github.com/microsoft/nni {need to test}
Tune: https://ray.readthedocs.io/en/latest/tune.html
AdaTune: https://github.com/awslabs/adatune
Hyperopt: https://github.com/hyperopt/hyperopt
Metric-learning: https://kevinmusgrave.github.io/pytorch-metric-learning/ (Not tested yet)

Cheatsheet:

Python: https://github.com/gto76/python-cheatsheet
Numpy: https://intellipaat.com/mediaFiles/2018/12/Python-NumPy-Cheat-Sheet-1.png
Pandas: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf
Tensorflow: https://aicheatsheets.com/static/pdfs/tensorflow_v_2.0.pdf
Pytorch: https://www.simonwenkel.com/publications/cheatsheets/pdf/cheatsheet_pytorch.pdf
Keras: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf

Visualization of data:

Mpld3 (matplotlib +d3js) http://mpld3.github.io/
Bokeh (stack multiple graph) http://bokeh.pydata.org/en/latest/
HoloViews (build visualization data structure) http://holoviews.org/
Plotly (2D,3D graph + js support) https://plot.ly/python/
Pygal (matplotlib + added style) http://www.pygal.org/en/latest/index.html
Altair (d3js alternative) https://github.com/altair-viz/altair

Machine Learning related to production and deploy:

Awesomeprod: https://github.com/EthicalML/awesome-production-machine-learning

Open-source repo for curated list:

https://github.com/amitness/toolbox
https://github.com/josephmisiti/awesome-machine-learning#natural-language-processing-10 https://github.com/sorend/awesome-python-machine-learning
Tools https://github.com/ml-tooling/best-of-ml-python

open-source projects/library related to NLP

Pre-processing text: clean-text, prenlp, meacb, textprocess, NLPre

Auto Exploratory data analysis

Pandas-profiling https://github.com/pandas-profiling/pandas-profiling
Sweetviz https://github.com/fbdesignpro/sweetviz
Auto vizualization https://github.com/AutoViML/AutoViz
Dtale https://github.com/man-group/dtale
Data prep https://github.com/sfu-db/dataprep
Quick EDA https://github.com/sid-the-coder/QuickDA
Sparmagic https://github.com/jupyter-incubator/sparkmagic

concurrency

pebble: https://pypi.org/project/Pebble/

Talks from top companies

Machine learning talks http://featurestore.org/