https://github.com/zsxkib/machine-learning-practical-cw2
BatchNorm'd VGG38 to Fix Vanishing Gradient on CIFAR100
https://github.com/zsxkib/machine-learning-practical-cw2
Last synced: 6 months ago
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BatchNorm'd VGG38 to Fix Vanishing Gradient on CIFAR100
- Host: GitHub
- URL: https://github.com/zsxkib/machine-learning-practical-cw2
- Owner: zsxkib
- Created: 2020-12-03T15:26:35.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2020-12-03T15:36:53.000Z (almost 5 years ago)
- Last Synced: 2025-04-01T13:08:36.847Z (6 months ago)
- Language: Jupyter Notebook
- Size: 43 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Machine Learning Practical
This repository contains the code for the University of Edinburgh [School of Informatics](http://www.inf.ed.ac.uk) course [Machine Learning Practical](http://www.inf.ed.ac.uk/teaching/courses/mlp/).
This assignment-based course is focused on the implementation and evaluation of machine learning systems. Students who do this course will have experience in the design, implementation, training, and evaluation of machine learning systems.
The code in this repository is split into:
* a Python package `mlp`, a [NumPy](http://www.numpy.org/) based neural network package designed specifically for the course that students will implement parts of and extend during the course labs and assignments,
* a series of [Jupyter](http://jupyter.org/) notebooks in the `notebooks` directory containing explanatory material and coding exercises to be completed during the course labs.
## Remote working
If you are working remotely, follow this [guide](notes/remote-working-guide.md).
## Getting set up
Detailed instructions for setting up a development environment for the course are given in [this file](notes/environment-set-up.md). Students doing the course will spend part of the first lab getting their own environment set up.