Fuzzy Logic, Machine Learning and Deep Learning with Julia

This repository contains slides, labs and code examples for using Julia to implement some artificial intelligence related algorithms. Codes run on top of a Docker image, ensuring a consistent and reproducible environment.

To run the code, you will need to first pull the Docker image by running the following command:

docker pull abmhamdi/jlai

This may take a while, as it will download and install all necessary dependencies.

How to control the containers:

• docker-compose up starts the container
• docker-compose down stops and destroys the container

Services can be run by typing the command docker-compose up. This will start the Jupyter Lab on http://localhost:2468 and you should be able to use Julia from within the notebook by starting a new Julia notebook. You can parallelly start Pluto on http://localhost:1234.

Included Algorithms

The repository includes implementation of the following algorithms:

1. Linear Regression, Logistic Regression, k-NN, SVM, K-MEANS
2. Fuzzy Inference System (FIS), Fuzzy Logic Controller
3. ANN, CNN, GAN, VAE, NLP
4. Transfer Learning
5. Reinforcement Learning

Prerequisites

You will need to have Docker installed on your machine. You can download it from the Docker website.

License

This project is licensed under the MIT License - see the LICENSE file for details.