PALS0039代做、代写Java/C++语言程序

日期：2025-03-22 03:41

PALS0039 Introduction to Deep Learning for Speech and Language Processing

Year: 2024-2025 Assessment: Coursework

Period: Central Assessment Weighting: 80%

Level: UG6, UG7 and PG7 Word count: 2500 words maximum (2000 text + 500

code)

Component: 001 Deadline: Monday, 28 April 2025

Please ensure you read and follow the Coursework Submission and Penalties page

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

Make sure you read the whole description, including the marking criteria

Autocompletion:

Humans can complete words, sentences (and even sounds) when parts were lost or were

masked by noise. Likewise, text-editing programmes can make suggestions for the text that

follows. This is what you will be doing in this task:

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

1. Coding of the implementation, including in-code comments, description of the code

and demonstration of knowledge about deep learning models (50%)

2. Presentation of the results (20%)

3. Discussion of outcomes and conclusions of study (20%)

4. Use of Jupyter/Colab notebook and conciseness of presentation (10%)

Note that there are differences in the standard marking scheme used for level 6 and level 7

submissions.