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Year: 2024-2025 |
Assessment: Coursework |
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Period: Central Assessment |
Weighting: 80% |
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Level: UG6, UG7 and PG7 |
Word count: 2500 words maximum (2000 text + 500 code) |
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Component: 001 |
Deadline: Monday, 28 April 2025 |
AI usage: You are allowed to use AI to assist with generating code. You are not allowed to use AI for any other purpose. Whether you use AI to assist with generating code or not, you need to demonstrate that you understand the code, no marks will be given for code that is not explained in your own words.
Coursework Description
Use deep learning to build a model that predicts the next three characters (e.g., “Merry Christ…” -> “mas”). Evaluate the training and performance of the model. Present the code in a manner that makes it easy to use for others. In your discussion, comment on why you chose your model and parameters. A good discussion presents further architectures and why you did not choose them. If the model does not perform well, explain what would be needed to improve it. Marking (see below) will be based on the design, implementation and evaluation of the deep learning approach, not necessarily on the accuracy achieved.
For your database, you can choose or combine from any of the ebooks that are uploaded to Moodle in the assignment section. Your model must not have used any other data. It is your task to create appropriate training and test sets from the data provided.
Submission requirements
- You should implement a working deep learning application as a Jupyter or Google Colab Notebook.
- The notebook should contain text and code. The text should provide all the necessary background, references, method, results analysis and discussion to explain the task as you might put in a lab report. The code should at a minimum demonstrate loading and processing of data, building a deep learning model and evaluation of its performance.
- The solution should be original – that is, you should motivate your own design decisions, not simply follow advice found on the web.
- No marks will be given on code alone. You need to demonstrate your understanding of the code and your choices.
- It is not necessary to obtain state of the art performance on the task. The goal is to show that you know how to design, implement and run a deep learning task in speech or language.
- For submission, you should run the notebook so that all text, code and outputs are visible, then save the whole as a PDF file for submission. The pdf file will be marked. The notebook itself should be submitted as an appendix or be linked and available during the marking period.
- The use of tables and figures is encouraged, and contributes to a good presentation of the results.
- The overall length of the text in the notebook (excluding code, comments in the code, outputs and bibliography) should be around 1500 words and must not exceed 2000 words. Penalties will apply from 2001 words.
- You should use comments in the code to adhere with good coding practice. The code and in-code comments count as a nominal 500 words but you may exceed this without penalty (though see point 4 of marking criteria, conciseness of presentation).
Marking Criteria
Note that there are differences in the standard marking scheme used for level 6 and level 7 submissions.