https://github.com/toelt-llc/course-deep-learning-meet-astrophysics

This repository contains the material for the workshop held at ETH (Zürich) on the 22nd January 2020.
https://github.com/toelt-llc/course-deep-learning-meet-astrophysics

deep-learning machine-learning physics teaching

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This repository contains the material for the workshop held at ETH (Zürich) on the 22nd January 2020.

• Host: GitHub
• URL: https://github.com/toelt-llc/course-deep-learning-meet-astrophysics
• Owner: toelt-llc
• Created: 2020-01-20T11:52:48.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2020-01-21T13:47:28.000Z (about 5 years ago)
• Last Synced: 2024-11-07T23:34:17.829Z (3 months ago)
• Topics: deep-learning, machine-learning, physics, teaching
• Homepage:
• Size: 24.3 MB
• Stars: 7
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Deep Learning meets (Astro)physics

Organizers:

Timothy Gebhard ([[email protected]](mailto:[email protected]))

Prof. Dr. S. Quanz ([Homepage](https://www.phys.ethz.ch/the-department/people/person-detail.MTY1MzQ3.TGlzdC84NDIsMTE3MjU5OTI5OQ==.html))

Umberto Michelucci ([[email protected]](mailto:[email protected]))

This repository contains the material for the workshop held at ETH (Zürich) on the 22nd January 2020.

## Sources and Books

The material is based on the two books:

**Applied Deep Learning - A Case-Based Approach to Understanding Deep Neural Networks**

By Umberto Michelucci

-- [Book webpage](http://toe.lt/z)

**Advanced Applied Deep Learning - Convolutional Neural Networks and Object Detection**

By Umberto Michelucci -- [Book webpage](http://toe.lt/10)

The material shown in the slides can be found in the two books with much more details and explanation.

## Slides

Slides are available as google slides and can be accessed at the following links

**Chalk Talk Neural Networks**: [https://docs.google.com/presentation/d/1MyUj3224opjqwJF1P40EUuRjcQIsgRMApWtWUeqP4b8/edit?usp=sharing](https://docs.google.com/presentation/d/1MyUj3224opjqwJF1P40EUuRjcQIsgRMApWtWUeqP4b8/edit?usp=sharing)

**Introduction to TensorFlow 2.0**: [https://docs.google.com/presentation/d/14SnqX70ZDwwP_texk3r2wJMBLWW3ZaiQolQo8KoHtxs/edit?usp=sharing](https://docs.google.com/presentation/d/14SnqX70ZDwwP_texk3r2wJMBLWW3ZaiQolQo8KoHtxs/edit?usp=sharing)

**Advanced Topics**: [https://docs.google.com/presentation/d/1kn6pKhGxydb3_mos5ksQ4uGBj0S5b9Tdzvf1MbJV6X8/edit?usp=sharing](https://docs.google.com/presentation/d/1kn6pKhGxydb3_mos5ksQ4uGBj0S5b9Tdzvf1MbJV6X8/edit?usp=sharing)

**Chalk Talk Convolutional Neural Networks**: [https://docs.google.com/presentation/d/1nWEzUPHgouG8c0uY_CSLMhsqfP84QDFdmTeR_0EaeSI/edit?usp=sharing](https://docs.google.com/presentation/d/1nWEzUPHgouG8c0uY_CSLMhsqfP84QDFdmTeR_0EaeSI/edit?usp=sharing)

(C) 2020 Umberto Michelucci, [www.toelt.ai](www.toelt.ai)

## Hands-on Notebooks

All the hands-on Jupyter notebooks can be run on Google Colab, so no need to install anything locally on your laptop. Simply click on the links below and try the notebooks.

**First Fully Connected Network with Keras**: [https://colab.research.google.com/drive/1TH8CPLwHeYZ5Fd-BLYm9NCeDYcqvSDa6](https://colab.research.google.com/drive/1TH8CPLwHeYZ5Fd-BLYm9NCeDYcqvSDa6)

**First example of CNN with Keras**: [https://colab.research.google.com/drive/1JMadgcCdcvpvfxH2eWnYnu99R-uW6qUg](https://colab.research.google.com/drive/1JMadgcCdcvpvfxH2eWnYnu99R-uW6qUg)

**Image classification with TF Hub**: [https://colab.research.google.com/drive/1pDmpZrGQuymnLkE_eTE-CHtjb39mQ5QB](https://colab.research.google.com/drive/1pDmpZrGQuymnLkE_eTE-CHtjb39mQ5QB)

**Keras functional API with TF 2.0**: [https://colab.research.google.com/drive/1xZxbHAzbr53OxFcHMAjOZlfv7_jyntzy](https://colab.research.google.com/drive/1xZxbHAzbr53OxFcHMAjOZlfv7_jyntzy)

**MNIST Classification with TF2.0**: [https://colab.research.google.com/drive/14OoNrvzPQhWiKBMr60Ab8soavE9NATIp](https://colab.research.google.com/drive/14OoNrvzPQhWiKBMr60Ab8soavE9NATIp)

**Pre-trained models with Keras**: [https://colab.research.google.com/drive/1oTdXeEriPdSVFC5v0qjEhbIEJP_zszXg](https://colab.research.google.com/drive/1oTdXeEriPdSVFC5v0qjEhbIEJP_zszXg)

**Transfer Learning with Keras**: [https://colab.research.google.com/drive/1XRcr2v2HFD8AV5GFNP0fxutHtqGdV61a](https://colab.research.google.com/drive/1XRcr2v2HFD8AV5GFNP0fxutHtqGdV61a)

**Neural Style Transfer with Keras**: [https://colab.research.google.com/drive/11Cc3oyFRefWToDq021SkSOI7JA24KfMU](https://colab.research.google.com/drive/11Cc3oyFRefWToDq021SkSOI7JA24KfMU)