Deep Learning tutorials in jupyter notebooks.

Overview

DeepSchool.io

License Binder

Sign up here 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.
  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 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 to request an invitation to the channel.
Owner
Sachin Abeywardana
PhD in machine learning. TensorFlow and PyMC3 enthusiast.
Sachin Abeywardana
Code for the paper: Sketch Your Own GAN

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ใ€CVPR 2021, Variational Inference Framework, PyTorchใ€‘ From Rain Generation to Rain Removal

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WOOD Implementation of our recent paper, WOOD: Wasserstein-based Out-of-Distribution Detection. Abstract The training and test data for deep-neural-ne

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Unofficial PyTorch Implementation of "DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features"

Pytorch Implementation of Deep Orthogonal Fusion of Local and Global Features (DOLG) This is the unofficial PyTorch Implementation of "DOLG: Single-St

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Repo for our ICML21 paper Unsupervised Learning of Visual 3D Keypoints for Control

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The implementation of the CVPR2021 paper "Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes"

STAR-FC This code is the implementation for the CVPR 2021 paper "Structure-Aware Face Clustering on a Large-Scale Graph with 10^7 Nodes" ๐ŸŒŸ ๐ŸŒŸ . ๐ŸŽ“ Re

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MogFace: Towards a Deeper Appreciation on Face Detection

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