https://github.com/sachinruk/deepschool.io

Deep Learning tutorials in jupyter notebooks.
https://github.com/sachinruk/deepschool.io

deep-learning jupyter-notebook keras python3

Last synced: 27 days ago
JSON representation

Deep Learning tutorials in jupyter notebooks.

• Host: GitHub
• URL: https://github.com/sachinruk/deepschool.io
• Owner: sachinruk
• License: other
• Created: 2016-12-04T11:10:09.000Z (almost 8 years ago)
• Default Branch: master
• Last Pushed: 2023-03-25T01:06:42.000Z (over 1 year ago)
• Last Synced: 2024-02-15T01:31:29.675Z (9 months ago)
• Topics: deep-learning, jupyter-notebook, keras, python3
• Language: Jupyter Notebook
• Homepage:
• Size: 68.9 MB
• Stars: 1,796
• Watchers: 128
• Forks: 522
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • Contributing: Contributing.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• awesome-google-colab - DeepSchool.io - Deep Learning tutorials in jupyter notebooks. (Course and Tutorial)

README

        
# DeepSchool.io

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)

[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/sachinruk/deepschool.io/master)

## [Sign up here](https://www.udemy.com/course/machine-learning-and-data-science-2021/?referralCode=E79228C7436D74315787) for Udemy Course on Machine Learning (Use code DEEPSCHOOL-MARCH to get 85% off course).

## 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. Sign up to the Udemy course above.

2. Subscribe to our [YouTube channel here](http://www.youtube.com/user/sachinabey?sub_confirmation=1).

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

We run all our notebooks on google colab. In order to do this:

1. Get a google account.

2. Click on this [link](https://drive.google.com/open?id=1L9BPDSm6Y4jbljf5IsI102zgW49wNGem) to take you to the google Drive folder.

3. Go to the DL-Keras folder (or any other topic that you wish to learn).

4. Double click on the notebook and click on, 'open with colaboratory' (You need to haved signed into Google for this).

5. Click on the 'Runtime' tab at the top and change to python3 and GPU. Now you are all good to go.

## 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

You can ask questions and join the development discussion:

- On the DeepSchool-io Slack channel. Use [this link](https://intense-waters-64607.herokuapp.com/) to request an invitation to the channel.