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!
1. Regression (Documentation, Interface, Notebook 
)
- Linear Regression
- Ridge Regression
- Lasso Regression
- ElasticNet Regression
- Decision Tree
- Random Forest (Bagging)
- Gradient Boosting (Boosting)
- AdaBoost (Boosting)
- XGBoost (Boosting)
- LightGBM
- CatBoost
- Support Vector Regressor (SVR)
- K-Nearest Neighbors (KNN) Regressor
- Extra Trees Regressor
- Multilayer Perceptron (MLP) Regressor
2. Classification (Documentation, Interface, Notebook )
- Logistic Regression
- Decision Tree Classifier
- Random Forest Classifier (Bagging)
- Extra Trees Classifier
- Gradient Boosting Classifier (Boosting)
- AdaBoost Classifier (Boosting)
- XGBoost Classifier (Boosting)
- LightGBM Classifier (Boosting)
- CatBoost Classifier (Boosting)
- Support Vector Classifier (SVC)
- K-Nearest Neighbors (KNN) Classifier
- Multilayer Perceptron (MLP) Classifier
- GaussianNB (Naive Bayes Classifier)
- Linear Discriminant Analysis (LDA)
- Quadratic Discriminant Analysis (QDA)
Unsupervised Learning (Scikit-Learn) (Documentation, Interface, Notebook )
- Convolutional Neural Networks (CNN)
- Example CNN Architecture: TinyVGG (from CNN Explainer)
- Transfer Learning (using TorchVision)
- Faster R-CNN
- YOLO (You Only Look Once)
- SSD (Single Shot MultiBox Detector)
- U-Net
- DeepLab
- PSPNet
- CNN + RNN approach (or CNN + Transformer)
- Potential integration with NLP techniques
- DCGAN (Deep Convolutional Generative Adversarial Networks)
- StyleGAN
- Diffusion Models
- SimCLR (Simple Framework for Contrastive Learning of Visual Representations)
- BYOL (Bootstrap Your Own Latent)
- SwAV (Swapping Assignments Between Views)
- DINO (Self-Distillation with No Labels)
- CLIP (Contrastive Language–Image Pre-training)
- RNN (Vanilla Recurrent Neural Network)
- LSTM (Long Short-Term Memory)
- GRU (Gated Recurrent Unit)
- Encoder-Decoder models (e.g., BERT, GPT, etc.)
- Attention Mechanisms
- Text Classification (sentiment analysis, topic classification)
- Machine Translation
- Named Entity Recognition (NER)
- Text Summarization (extractive or abstractive)
- Question Answering
- Language Modeling (causal or masked)
- Seq2Seq with attention
- GPT-like for text generation
- Time Series Forecasting with RNNs
- Temporal Convolutional Networks (TCN)
- Transformers for Time Series
Q-Learning Deep Q-Networks (DQN)
REINFORCE (Policy Gradients) Actor-Critic (A2C, PPO, etc.)
Hierarchical RL Multi-Agent RL Offline RL (Batch RL)
- models/: Contains all the AI algorithm implementations, organized by category.
- data/: Includes datasets and data preprocessing utilities.
- utils/: Utility scripts and helper functions.
- scripts/: Executable scripts for training, testing, and other tasks.
- 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.
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 [email protected].
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!