UCL Module | CS | UCL Moodle Page

Term 2, Academic Year 2023-24

Module Lead
Yipeng Hu [email protected]

The module tutorials (see bellow) and coursework use Python, NumPy and an option between TensorFlow and PyTorch. The Development environment document contains details of the supported development environment, though it is not mandatory.

To run the tutorial examples, follow the instruction below.

First, set up the environment:

conda create --name comp0197-tf tensorflow pillow
conda activate comp0197-tf

conda create --name comp0197-pt pytorch torchvision
conda activate comp0197-pt

Additional libraries required for individual tutorials are specified in the readme file in each tutorial directory.

Scripts with "_tf" and "_pt" postfix are using TensorFlow 2 and PyTorch, respectively.

All visual examples will be saved in files, without requiring graphics.

Then, change directory cd to each individual tutorial folder and run individual training scripts, e.g.:

python train_pt.py   

or

python train_tf.py  

Image classification
Image segmentation

Text classification
Character generation

MNIST generation

Face image simulation

A collection of books and research papers is provided in the Reading List.