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https://github.com/ramene/awesome-deepnote

A curated list of extensions, python packages, machine learning and collaborative notebooks ready to run in Deepnote.
https://github.com/ramene/awesome-deepnote

List: awesome-deepnote

awesome awesome-list collections data-visualization datascience deep-learning deep-neural-networks deepnote gans hashicorp hashicorp-waypoint lists notebooks python scikit-learn visualizations

Last synced: 3 months ago
JSON representation

A curated list of extensions, python packages, machine learning and collaborative notebooks ready to run in Deepnote.

Host: GitHub
URL: https://github.com/ramene/awesome-deepnote
Owner: ramene
Created: 2020-10-09T20:31:00.000Z (about 4 years ago)
Default Branch: main
Last Pushed: 2020-10-15T05:42:40.000Z (about 4 years ago)
Last Synced: 2024-05-21T10:14:09.298Z (6 months ago)
Topics: awesome, awesome-list, collections, data-visualization, datascience, deep-learning, deep-neural-networks, deepnote, gans, hashicorp, hashicorp-waypoint, lists, notebooks, python, scikit-learn, visualizations
Homepage:
Size: 184 KB
Stars: 54
Watchers: 7
Forks: 5
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

ultimate-awesome - awesome-deepnote - A curated list of extensions, python packages, machine learning and collaborative notebooks ready to run in Deepnote. (Other Lists / PowerShell Lists)

README

        # Awesome Deepnote [![Awesome](https://awesome.re/badge.svg)](https://awesome.re)  [](https://github.com/SuNaden/deepnote-launch-example) 

A curated [awesome](https://github.com/topics/awesome) list of deepnote notebooks, extensions and resources. [Deepnote](http://deepnote.com) is a new kind of data science notebook. Jupyter-compatible with real-time collaboration and easy deployment.

#deepnote, #jupyter-kernels, #machine-learning


search:  deepnote








## General

#### _matplotlib, plotly_

-  [Visual data exploration with Virginia's public COVID-19 cases dataset](https://github.com/jammy-bot/va-covid-eda) by [Jamal Dargan](https://github.com/jammy-bot)

#### _anaconda, miniconda_

-  [Using Conda in Deepnote in 3 simple steps](https://beta.deepnote.com/project/1e061457-9c0a-412a-a8fa-c08358928ba2)

#### _scikit-learn, tensorflow, keras_

- [ ](https://deepnote.com/launch?template=deepnote&url=https%3A%2F%2Fgithub.com%2Fmatthew-e-thomas%2Fdeeptnote-credit-card-fraud%2Fblob%2Fmaster%2Fcredit_card_fraud_ml.ipynb)  [Detect Credit Card Fraud](https://github.com/matthew-e-thomas/deeptnote-credit-card-fraud)  

- [ ](https://deepnote.com/launch?template=deepnote&url=https%3A%2F%2Fgithub.com%2Falfarias%2Fcustomer-churn-prediction%2Fblob%2Fmaster%2Fnotebooks%2Fcustomer-churn-prediction.ipynb%29)  [Customer Churn Prediction](https://github.com/alfarias/customer-churn-prediction/blob/master/notebooks/customer-churn-prediction.ipynb) 

- [ ](https://deepnote.com/launch?template=data-science&url=https%3A//github.com/ageron/handson-ml/blob/master/02_end_to_end_machine_learning_project.ipynb)  [Hands-on Machine Learning with Scikit-Learn and TensorFlow](https://learning.oreilly.com/library/view/hands-on-machine-learning/9781491962282/)

- [ ](https://deepnote.com/launch?template=data-science&url=https%3A//github.com/ageron/handson-ml/blob/master/02_end_to_end_machine_learning_project.ipynb)   [Deep Learning with TensorFlow 2 and Keras](https://github.com/ageron/tf2_course)

#### _tensorboard_

-  [Tensorboard with ngrok](https://deepnote.com/project/d9ef0f3d-e2e3-40ef-8f40-2dc37fb22b88#%2Ftensorboard.ipynb) 

-  [Scraping the EPL Stats Website](https://deepnote.com/project/19f51d7b-ae79-4c51-906c-dee0138da144) –– [Docs](https://github.com/sportsdatasolutions/python_project_template/blob/master/getting_started_deepnote.md)

#### _collections, books, journals_

- [Intro to Deep Learning](https://www.kaggle.com/learn/intro-to-deep-learning) by [Ryan Holbrook](https://www.kaggle.com/ryanholbrook) @ **Kaggle**

- [Datascience IPython Notebooks](https://github.com/donnemartin/data-science-ipython-notebooks) by [Donne Martin](https://github.com/donnemartin)

-  [Maths: Form and Function with Python](https://github.com/James-G-Hill/Mathematics-Form-and-Function-Notebooks) by [James G. Hill](https://github.com/James-G-Hill) 

  - [ ](https://deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2FCamDavidsonPilon%2FProbabilistic-Programming-and-Bayesian-Methods-for-Hackers%2Fblob%2Fmaster%2FPrologue%2FPrologue.ipynb) [Probabilistic Programming and Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)

- [ ](https://deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Funpingco%2FPython-for-Probability-Statistics-and-Machine-Learning-2E%2Fblob%2Fmaster%2Fchapter%2Fmachine_learning%2Fintro.ipynb) [Python for Probability, Statistics, and Machine Learning 2E](https://github.com/unpingco/Python-for-Probability-Statistics-and-Machine-Learning-2E)

- [ ](https://deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Fmikhailklassen%2FMining-the-Social-Web-3rd-Edition%2Fblob%2Fmaster%2Fnotebooks%2FChapter%25200%2520-%2520Preface.ipynb) [Mining the Social Web](https://github.com/mikhailklassen/Mining-the-Social-Web-3rd-Edition/tree/master/notebooks) by [Mikhail Klassen](https://github.com/mikhailklassen)

## Integrations

-  [ColabCode](https://github.com/abhishekkrthakur/colabcode) by [Abhishek Thakur](https://github.com/abhishekkrthakur) –– [Video](https://youtu.be/7kTbM3D02jU)

## Resources

- [Deepnote Slack Community](https://join.slack.com/t/deepnotecommunity/shared_invite/enQtOTI4OTA1MzYwNTMzLTQ4ZGY4Y2VkOTZkYTNjY2U3NTU5ZjJjMDRiMmNmOTgzMzhmYjZlMTczZmY1MDhhM2RmMDk3OWYxM2MyZmFlMDc)

-  [Deepnote Launch Buttons](https://github.com/SuNaden/deepnote-launch-example) by [Filip Stollar](https://github.com/SuNaden) 

## Other Awesome Lists

 - [lists](https://github.com/jnv/lists)

 - [pytudes](https://github.com/norvig/pytudes)

 - [awesome-r](https://github.com/qinwf/awesome-R)

 - [awesome-aws](https://github.com/donnemartin/awesome-aws)

 - [awesome-dataviz](https://github.com/fasouto/awesome-dataviz)

 - [awesome-python](https://github.com/vinta/awesome-python)

 - [awesome-tensorflow](https://github.com/jtoy/awesome-tensorflow)

 - [awesome-datascience](https://github.com/academic/awesome-datascience)

 - [awesome-datascience-ideas](https://github.com/JosPolfliet/awesome-datascience-ideas)

 - [awesome-machine-learning](https://github.com/josephmisiti/awesome-machine-learning)

 - [awesome-public-datasets](https://github.com/caesar0301/awesome-public-datasets) 

 - [awesome-machine-learning-on-source-code](https://github.com/src-d/awesome-machine-learning-on-source-code)

 - [awesome-graph-classification](https://github.com/benedekrozemberczki/awesome-graph-classification)

 - [awesome-decision-tree-papers](https://github.com/benedekrozemberczki/awesome-decision-tree-papers)

 - [awesome-fraud-detection-papers](https://github.com/benedekrozemberczki/awesome-fraud-detection-papers)

 - [machine-learning-for-software-engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)

- [Glossary of common statistics and ML terms](https://www.analyticsvidhya.com/glossary-of-common-statistics-and-machine-learning-terms/) 

Other amazingly awesome lists can be found by searching: [#awesome](https://github.com/topics/awesome), [#awesome-lists](https://github.com/topics/awesome-lists)

#### In the media

-  [Deepnote: the modern way to teach Data Science](https://medium.com/@robertlacok/deepnote-the-modern-way-to-teach-data-science-99998ce659a)

-  [Collaborative notebooks for ML course at Cambridge](https://deepnote.com/article/university-of-cambridge)

-  [Reviewing Deepnote — The New IDE for Data Scientists](https://towardsdatascience.com/reviewing-deepnote-the-new-ide-for-data-scientists-90c3464ebc5e)

-  [Deepnote Emerges from Stealth: With YC, Index, and Accel Leading Our Seed Round](https://medium.com/deepnote/deepnote-emerges-from-stealth-with-yc-index-and-accel-leading-our-seed-round-12325281cde0)

### License

[![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)](http://creativecommons.org/licenses/by/4.0/)

This work is licensed under a [Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).