https://github.com/emilwallner/deep-learning-101
The tools and syntax you need to code neural networks from day one.
https://github.com/emilwallner/deep-learning-101
deep-learning dropout machine-learning regularization tflearn
Last synced: 5 months ago
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The tools and syntax you need to code neural networks from day one.
- Host: GitHub
- URL: https://github.com/emilwallner/deep-learning-101
- Owner: emilwallner
- License: mit
- Created: 2017-08-10T19:05:49.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-09-25T09:04:31.000Z (about 8 years ago)
- Last Synced: 2025-04-30T10:12:19.453Z (5 months ago)
- Topics: deep-learning, dropout, machine-learning, regularization, tflearn
- Language: Jupyter Notebook
- Homepage:
- Size: 11.7 KB
- Stars: 60
- Watchers: 4
- Forks: 15
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Deep Learning 101
When I started learning deep learning I spent two weeks researching. I selected tools, compared cloud services, and researched online courses. In retrospect, I wish I could have built neural networks from day one. [That’s what this article](http://blog.floydhub.com/my-first-weekend-of-deep-learning) is set out to do. You don’t need any prerequisites, yet a basic understanding of Python, the command line, and Jupyter notebook will help.
This is the code experiments from the article.