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.

Included Algorithms

The repository includes implementation of the following algorithms:

1. Fuzzy Logic, Fuzzy Inference Systems (FIS): Mamdani, Sugeno and Tsukamoto
2. Linear Regression, Logistic Regression, Naive Bayes, Decision Tree, k-NN, SVM, K-MEANS, and PCA
3. ANN, CNN, Transfer Learning, GAN, VAE, NLP
4. Reinforcement Learning

Note

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

docker pull abmhamdi/jlai-p1

This may take a while, as it will download source code, julia image, and all necessary system dependencies.

How to control the containers:

Services can be started by typing the command:

docker compose up -d # starts the containers in detached mode

docker compose down # stops and removes them

This will launch 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 use Pluto on http://localhost:1234.

Important

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.