anton/mlpractical
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity
244 Commits 2 Branches 0 Tags 91 MiB
Jupyter Notebook 51.4%
Python 25.8%
TeX 22.1%
Shell 0.7%
9bb4d7560b
Go to file
Matt Graham 9bb4d7560b Adding dropout and maxout layers to module.
2016-11-05 22:19:47 +00:00
courseworks Adding coursework files to master branch. 2016-10-14 03:56:06 +01:00
data Renaming MNIST eval set to test set for consistency with slides. 2016-10-14 03:36:16 +01:00
mlp Adding dropout and maxout layers to module. 2016-11-05 22:19:47 +00:00
notebooks Adding dropout and maxout notebook answers. 2016-11-05 22:19:28 +00:00
.gitignore 1st labs 2015-09-27 22:00:09 +01:00
environment-set-up.md Further clarifying reload vs. import note. 2016-09-27 19:15:28 +01:00
getting-started-in-a-lab.md Adding lab start up instructions. 2016-10-06 12:30:05 +01:00
quota-issue.md Changing to using remove rather than clean in quota issue fix based on lab feedback. 2016-09-27 19:02:22 +01:00
README.md Small fixes to initial description. 2016-09-20 12:54:51 +01:00
running-notebooks-remotely.md Adding instructions on running notebooks remotely. 2016-11-02 02:55:10 +00:00
secure-notebook-server.sh Adding bash script to secure Jupyter notebook server on DICE. 2016-10-27 21:35:55 +01:00
setup.py Adding additional authors to metadata. 2016-09-21 00:53:52 +01:00

README.md

Machine Learning Practical

This repository contains the code for the University of Edinburgh School of Informatics course Machine Learning Practical.

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 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 notebooks in the notebooks directory containing explanatory material and coding exercises to be completed during the course labs.

Getting set up

Detailed instructions for setting up a development environment for the course are given in this file. Students doing the course will spend part of the first lab getting their own environment set up.