Deep Learning tutorials in jupyter notebooks.
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README.md

DeepSchool.io

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Goals

  1. Make Deep Learning easier (minimal code).
  2. Minimise required mathematics.
  3. Make it practical (runs on laptops).
  4. Open Source Deep Learning Learning.
  5. Grow a collaborating practical community around DL.
  6. Memes: No seriously. Make DL fun and interactive, this means more Trump tweets.

Support Us

There's a few ways you can support this initiative:

  1. Right now this is very much a self funded project. If you wish to see more and more high quality tutorials and videos support us at: https://www.patreon.com/deepschoolio
  2. Subscribe to our YouTube channel here.
  3. Star this repository and share it!

Contents

The following contents are each contained within a folder:

  1. Data Science (eg. Pandas)
  2. Deep Learning (Keras)
  3. Bayesian Learning (PyMC3)

Installation

If you are a beginner (haven't done CNNs yet) simply click this link instead of following the installation comands below. It launches a live notebook server with these notebooks using binder: Binder

  1. Install Docker https://www.docker.com/
  2. Use the following commands to run from docker1.
git clone https://github.com/sachinruk/deepschool.io.git
cd deepschool.io
bash run.sh
  1. Now go to localhost:9000 on your browser to start using the jupyter notebooks.
  2. (Optional) If you are on a mac/windows some of the examples may not work because the docker image may run out of memory. Hence under preferences in docker there is the option to increase the allocated memory. I have set it to 8GB. Run bash run.sh again if you reset memory.

See here for installing on windows.

Support

You can ask questions and join the development discussion:

Meetup

First meetup node: https://www.meetup.com/DeepSchool-io/

YouTube playlist

Find the corresponding video tutorial here (not all notebooks have an associated video) https://www.youtube.com/playlist?list=PLIx9QCwIhuRS1SPS9LHF7VjvZyM1g2Swz

Notes

1: Refer to this Dockerfile and this for information on how the docker image was built.