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Instructions

Exercises for practical sessions and projet work of Artificial Intelligence and Deep Learning

Academic year: 2024-2025

Exercises

Objectives

The objective of the practical work is to create a project to understand the different aspects of artificial intelligence and deep learning. Following the completion of all the exercises, you will be able to

  • implement the following topics:
    • symbolic artificial intelligence
    • artificial neural network
    • Deep learning
  • create neural network models using different activation structures and functions
  • configure and optimize neural networks
  • test existing models of recurrent neural networks such as LSTM and convolutional neural networks
  • write and execute simple programs in the Prolog programming language

Instructions

  1. All practical work is based on the aspects you have already seen during your course. Make good use of your course materials.
  2. Work in pairs.
  3. It is mandatory to cite all sources (e.g. internet, groups).
  4. The sessions are supervised by 2 teachers.

Evaluation

  1. Practical work corresponds to 40% of your final grade.
  2. You have two assignments and a project. Each practical work consists of several exercises. Each exercise is graded.
  3. Total points for all the assignments and the project: 20
  4. Online submission
  5. Each question has a difficulty level
    • ★: Easy
    • ★★: Average difficulty
    • ★★★: Difficult

Submission

There are two parts of the report: self-evaluation report and sources.

Your rendering folder should contain the following files:

a. README: self-assessment report
b. CONTRIBUTORS: Names and first names of contributors
c. src/: the source code in the src directory

Your submission must be renamed as group_N1_N2, where N1 and N2 are the names (e.g., group_DUPONT_SMITH.).

Write README and CONTRIBUTORS in markdown format.

The contents of README (or self-assessment report): TP N (N: [1..2])

i. Libraries
ii. References: URLs, groups
iii. Difficulty: level of difficulty (easy, medium, difficult)
iv. Comments (optional): remarks etc.

Check list

Before submitting your practical work, verify this checklist:

  • ✅ The names (first name and last names) of the pair are present in the CONTRIBUTORS file
  • ✅ The README file is completely filled.
  • ✅ Your code is well commented.
  • ✅ Your code can be executed without any error (and if possible, without any warning).

Code template

For reference, you can consult the following repository: https://github.com/johnsamuelwrites/IA-DeepLearning. It contains code templates for all the exercises of both the practical work and the project.

You can view it online using the link above or clone it on your machine using the following commands on the terminal.

          $ git clone https://github.com/johnsamuelwrites/IA-DeepLearning
          $ cd IA-DeepLearning
          $ ls
          $ cat en/README.md

You can run the following command for obtaining the latest modifications on the repository:

          $ git pull