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Machine Learning Practical - Coursework 2: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.

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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.

Remote working

If you are working remotely, follow this guide.

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.

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Machine Learning Practical - Coursework 2: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.

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