Under Development
Welcome to AI-Algorithms-Made-Easy! This project is a comprehensive collection of artificial intelligence algorithms implemented from scratch using PyTorch. Our goal is to demystify AI by providing clear, easy-to-understand code and detailed explanations for each algorithm.
Whether you're a beginner in machine learning or an experienced practitioner, this project offers resources to enhance your understanding and skills in AI.
AI-Algorithms-Made-Easy aims to make AI accessible to everyone by:
- Intuitive Implementations: Breaking down complex algorithms into understandable components with step-by-step code.
- Educational Notebooks: Providing Jupyter notebooks that combine theory with practical examples.
- Interactive Demos: Offering user-friendly interfaces built with Gradio to experiment with algorithms in real-time.
- Comprehensive Documentation: Supplying in-depth guides and resources to support your AI learning journey.
Our mission is to simplify the learning process and provide hands-on tools to explore and understand AI concepts effectively.
This project is currently under development. Stay tuned for updates!
- Linear Regression
- Logistic Regression
- Decision Trees (DTs)
- Random Forests (RF)
- Support Vector Machines (SVM)
- K-Nearest Neighbors (K-NN)
- Gradient Boosting Machines (GBMs)
- Multilayer Perceptrons (MLP)
- K-Means Clustering
- Principal Component Analysis (PCA)
- Hierarchical Clustering
- Autoencoders
- Isolation Forest
- Gaussian Mixture Models
- Convolutional Neural Networks (CNN)
- Recurrent Neural Networks (RNN)
- Long Short-Term Memory Networks (LSTM)
- Gated Recurrent Unit (GRU)
- Generative Adversarial Networks (GAN)
- Transformers
- Attention Mechanisms
- Image Classification/Transfer learning (TL)
- Object Detection
- Semantic Segmentation
- Style Transfer
- Image Captioning
- Generative Models
- Sentiment Analysis (SA)
- Machine Translation
- Named Entity Recognition (NER)
- Text Classification
- Text Summarization
- Question Answering
- Language Modeling
- Transformer Models
- Time Series Forecasting with RNNs
- Temporal Convolutional Networks (TCNs)
- Transformers for Time Series
- Q-Learning
- Deep Q-Networks (DQN)
- Policy Gradients
- Actor-Critic Methods
- Proximal Policy Optimization
- models/: Contains all the AI algorithm implementations, organized by category.
- data/: Includes datasets and data preprocessing utilities.
- utils/: Utility scripts and helper functions.
- interfaces/: Interactive applications using Gradio and web interfaces.
- notebooks/: Jupyter notebooks for tutorials and demonstrations.
- deploy/: Scripts and instructions for deploying models.
- website/: Files related to the project website.
- docs/: Project documentation.
- examples/: Example scripts demonstrating how to use the models.
Installation instructions will be provided once the initial release is available.
Usage examples and tutorials will be added as the project develops.
We welcome contributions from the community! To contribute:
- Fork the repository on GitHub.
- Clone your fork to your local machine.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them with descriptive messages.
- Push your changes to your forked repository.
- Open a pull request to the main repository.
Please read our Contributing Guidelines for more details.
This project is licensed under the MIT License - see the LICENSE file for details.
For questions, suggestions, or feedback:
- GitHub Issues: Please open an issue on the GitHub repository.
- Email: You can reach us at m.boukabous95@gmail.com.
Thank you for your interest in AI-Algorithms-Made-Easy! We are excited to build this resource and appreciate your support and contributions.
- PyTorch: For providing an excellent deep learning framework.
- Gradio: For simplifying the creation of interactive demos.
- OpenAI's ChatGPT: For assistance in planning and drafting project materials.
- Watch this repository for updates.
- Star the project if you find it helpful.
- Share with others who might be interested in learning AI algorithms.
Let's make AI accessible and easy to learn for everyone!