https://github.com/natbusa/deepnumbers

A set of educational deep learning demos applied to the MNIST dataset
https://github.com/natbusa/deepnumbers

deep-learning-tutorial deep-neural-networks machine-learning

Last synced: 9 days ago
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A set of educational deep learning demos applied to the MNIST dataset

• Host: GitHub
• URL: https://github.com/natbusa/deepnumbers
• Owner: natbusa
• License: apache-2.0
• Created: 2016-12-31T13:01:28.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2017-04-18T13:08:20.000Z (about 8 years ago)
• Last Synced: 2025-04-26T08:07:34.881Z (19 days ago)
• Topics: deep-learning-tutorial, deep-neural-networks, machine-learning
• Language: Jupyter Notebook
• Size: 3.39 MB
• Stars: 9
• Watchers: 1
• Forks: 11
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# deepnumbers

A set of educational deep learning demos applied to the MNIST dataset

## Slides

You can find a presentation about this work at:  

https://www.slideshare.net/natalinobusa/7-steps-for-highly-effective-deep-neural-networks

Hires pdf available here:

https://drive.google.com/file/d/0BwNrPuGaMi8PbVhUYUVKWUhGRjQ



  



## Brighttalk

I do have a webinar on this one, thanks to the folks at Brighttalk.  

Check https://www.brighttalk.com/webcast/8251/252545

## Youtube

I thinking of taking screen captures of this project and posting it on youtube. So far my attempts have not been super successful (kudos to those pro youtubers out there - it's *definitely* not as easy as it looks).

## Regression

## SLP



  

  



## MLP



  



## Convolutional



  

  



## Batch Normalization



  



## Inception



  



## Residual



  



## LSTM on images