Skip to content

Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, Apptainer, and more.

License

Notifications You must be signed in to change notification settings

lcong/deep-learning-wizard

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Deep Learning Materials by Deep Learning Wizard

DOI

Start Learning Now

Please head to www.deeplearningwizard.com to start learning! It is mobile/tablet friendly and open-source.

Repository Details

This repository contains all the notebooks and mkdocs markdown files of the tutorials covering machine learning, deep learning, deep reinforcement learning, data engineering, general programming, and visualizations powering the website.

Take note this is an early work in progress, do be patient as we gradually upload our guides.

Sections and Subsections

About Deep Learning Wizard

We deploy a top-down approach that enables you to grasp deep learning theories and code easily and quickly. We have open-sourced all our materials through our Deep Learning Wizard Wikipedia. For visual learners, feel free to sign up for our video course and join thousands of deep learning wizards.

To this date, we have taught thousands of students across more than 120+ countries.

Contribution

We are openly calling people to contribute to this repository for errors. Feel free to create a pull request.

Main Contributor

Ritchie Ng

Editors and Supporters

Bugs and Improvements

Feel free to report bugs and improvements via issues. Or just simply try to pull to make any improvements/corrections.

Social Media

Citation

If you find the materials useful, like the diagrams or content, feel free to cite this repository.

DOI

About

Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, Apptainer, and more.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 48.0%
  • HTML 46.6%
  • JavaScript 5.2%
  • Shell 0.2%