https://github.com/lukasmosser/londonhack_pytorch
https://github.com/lukasmosser/londonhack_pytorch
Last synced: about 2 months ago
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- Host: GitHub
- URL: https://github.com/lukasmosser/londonhack_pytorch
- Owner: LukasMosser
- License: mit
- Created: 2019-05-30T15:03:52.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-10-18T18:05:00.000Z (about 6 years ago)
- Last Synced: 2024-12-31T21:12:44.508Z (12 months ago)
- Language: Jupyter Notebook
- Size: 2.07 MB
- Stars: 11
- Watchers: 3
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Londonhack 2019 - Pytorch Tutorial
### Tutorial Notebooks for the Pytorch Tutorial at the London Hack 2019
### Disclaimer
This material is not intended to be a full course on machine, deep learning, or neural networks, and is meant to introduce basic Pytorch functionality based on a number of examples.
Pre-requisites are:
- Basic Linear Algebra
- Experience with Python Programming and the scientific python stack (Numpy, Matplotlib, ...) is recommended.
- Some familiarity with Neural Networks, Optimization, Convolutional Neural Networks and their concepts.
All code is meant to be run on Google Colab and was built on Pytorch 1.0.
## Course Material
| Session | Exercise (Colab) | Solutions (Colab) |
|-----------|------------------|-----------------------|
| Getting Started: Google Colab and Logistics | [Exercise](session_0/Getting_Started.ipynb) [](http://colab.research.google.com/github/LukasMosser/londonhack_pytorch/blob/master/session_0/Getting_Started.ipynb) | |
| Session 1: Pytorch, Automatic Differentiation, Neural Nets | [Exercise](session_1/Londonhack-Session-1-Pytorch-Autograd-Optimization-Neural-Networks-Exercise.ipynb) [](http://colab.research.google.com/github/LukasMosser/londonhack_pytorch/blob/master/session_1/Londonhack-Session-1-Pytorch-Autograd-Optimization-Neural-Networks-Exercise.ipynb) | [Solutions](https://github.com/LukasMosser/londonhack_pytorch/blob/master/session_1/Londonhack-Session-1-Pytorch-Autograd-Optimization-Neural-Networks-Solutions.ipynb) [](https://colab.research.google.com/github/LukasMosser/londonhack_pytorch/blob/master/session_1/Londonhack-Session-1-Pytorch-Autograd-Optimization-Neural-Networks-Solutions.ipynb) |
| Session 2: Training Deep Neural Networks | [Exercise](session_2/Londonhack-Session-2-MNIST-Neural-Networks-Regularization-Cross-Validation-Exercise.ipynb) [](http://colab.research.google.com/github/LukasMosser/londonhack_pytorch/blob/master/session_2/Londonhack-Session-2-MNIST-Neural-Networks-Regularization-Cross-Validation-Exercise.ipynb) | [Solutions](https://github.com/LukasMosser/londonhack_pytorch/blob/master/session_2/Londonhack-Session-2-MNIST-Neural-Networks-Regularization-Cross-Validation-Solutions.ipynb) [](https://colab.research.google.com/github/LukasMosser/londonhack_pytorch/blob/master/session_2/Londonhack-Session-2-MNIST-Neural-Networks-Regularization-Cross-Validation-Solutions.ipynb) |
| Session 3: Convolutional Neural Networks | [Exercise](session_3/Londonhack-Session-3-FromConvolutions-To-ConvNets-Exercise.ipynb) [](http://colab.research.google.com/github/LukasMosser/londonhack_pytorch/blob/master/session_3/Londonhack-Session-3-FromConvolutions-To-ConvNets-Exercise.ipynb) | [Solutions](https://github.com/LukasMosser/londonhack_pytorch/blob/master/session_3/Londonhack-Session-3-FromConvolutions-To-ConvNets-Solutions.ipynb) [](https://colab.research.google.com/github/LukasMosser/londonhack_pytorch/blob/master/session_3/Londonhack-Session-3-FromConvolutions-To-ConvNets-Solutions.ipynb) |
| Project: The Seismic-NIST Dataset | [Dataset](https://github.com/LukasMosser/SNIST) | [Benchmark](https://github.com/LukasMosser/SNIST/blob/master/benchmarks/SNIST_Benchmark_Roeth_and_Tarantola.ipynb) |