Londonhack 2019 - Pytorch Tutorial
Tutorial Notebooks for the Pytorch Tutorial at the London Hack 2019
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.
|Session||Exercise (Colab)||Solutions (Colab)|
|Getting Started: Google Colab and Logistics||Exercise|
|Session 1: Pytorch, Automatic Differentiation, Neural Nets||Exercise||Solutions|
|Session 2: Training Deep Neural Networks||Exercise||Solutions|
|Session 3: Convolutional Neural Networks||Exercise||Solutions|
|Project: The Seismic-NIST Dataset||Dataset||Benchmark|