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Coursework 1 description
The aim of this coursework is to explore the classification of images of handwritten digits using neural networks. The first part of the coursework will concern the implementation of different activation functions (discussed in lecture 4) in the MLP framework. The second part will involve exploring and analysing different multi-layer neural network architectures. The coursework uses an extended version of the MNIST dataset, the EMNIST Balanced Dataset.
The code for the coursework is available on the course Github repository on a branch mlp2019-20/coursework_1. This also includes a LaTeX template (see the report directory) for the report that you will need to submit. Full details in the Coursework 1 document.
Coursework 2 description
The aim of this coursework is to further explore the classification of images using convolutional neural networks on CIFAR100 dataset. CIFAR100 consists of 60,000 32x32 colour images in 100 classes, with 600 images per class. The first part of the coursework will concern the implementation of convolutional networks using the MLP framework. The second part involves debugging and fixing a "broken" neural network, and then subsequently enhancing the resulting (now healthy) network to improve its generalization performance.
In order to support the experiments you will need to run for the second part of the coursework (which will be carried out in PyTorch we have acquired Google Cloud Platform credits which allow the use of the Google Compute Engine infrastructure. Each student enrolled on the MLP course will receive a $50 Google Cloud credit coupon which is enough to carry out the experiments required for this coursework. You will receive an email which will give you the URL you will need to access in order to request your Google Cloud Platform coupon.
The provided code and setup information for this coursework is available on the course
Github repository on a branch mlp2019-20/coursework_2. This also includes a LaTeX template (see the report directory) for the report that you will need to submit. Full details in the Coursework 2 document.
MLP in semester 2 will be based on projects done in groups of 2-3 students. The projects, which should be done using one of the deep learning toolkits (TensorFlow or PyTorch are recommended) can be chosen from a variety of topics and data sets. These projects correspond to courseworks 3 and 4.
Please remember the University requirement as regards all assessed work for credit. Details about this can be found on the page describing the academic misconduct policy.
Furthermore, you are required to take reasonable measures to protect your assessed work from unauthorised access. For example, if you put any such work on a public repository then you must set access permissions appropriately (generally permitting access only to yourself, or your group in the case of group practicals).
Copyright (c) University of Edinburgh 2015-2019
The MLP course material is licensed under the
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
licence.txt
The software at https://github.com/CSTR-Edinburgh/mlpractical is licensed under the Modified BSD License.
This page maintained by Hakan Bilen.
Last updated: 2020/03/16 12:17:29UTC
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