AI Libraries Cheatsheet

Table of Contents

• Introduction
• Features
• Pros
• Libraries Covered
• Usage
• Folder Structure
• Contributing
• License
• Acknowledgements

Introduction

Welcome to the AI Libraries Cheatsheet repository! This project is meticulously crafted to be an indispensable resource for machine learning developers and data scientists. By offering concise, well-organized, and up-to-date references, this repository aims to enhance productivity and foster a deeper understanding of essential AI libraries.

Features

• Comprehensive Reference: Covers a wide range of AI libraries, including model development, computer vision, NLP, and more.
• Organized Structure: Categorized by functionality to facilitate quick access to relevant information.
• Practical Examples: Includes usage snippets and practical examples for immediate application.
• Community-Driven: Open to contributions and regularly updated with the latest developments in AI.

Pros

• Time-Saving: Quickly find information without wading through extensive documentation.
• Enhanced Learning: Learn through concise examples and best practices.
• Up-to-Date: Continuously updated to reflect the latest advancements and library versions.
• Collaborative: Encourages contributions from the community to keep the resource robust and current.

Libraries Covered

AI Model Development

• TensorFlow: End-to-end open-source platform for machine learning.
• PyTorch: Open source machine learning framework that accelerates the path from research to production.

Computer Vision

• OpenCV: Library of programming functions mainly aimed at real-time computer vision.
• scikit-image: Collection of algorithms for image processing.

Data Analysis and Manipulation

• Pandas: Data structures and data analysis tools.
• NumPy: Fundamental package for scientific computing with Python.

Data Visualization

• Matplotlib: Comprehensive library for creating static, animated, and interactive visualizations.
• Seaborn: Statistical data visualization based on Matplotlib.

Deep Learning & Machine Learning

• Keras: High-level neural networks API, written in Python.
• Scikit-learn: Simple and efficient tools for predictive data analysis.

Generative Models

• GANs: Generative Adversarial Networks for generating realistic data.
• VAEs: Variational Autoencoders for generating new, similar data.

Large Language Models (LLMs)

• GPT: Generative Pre-trained Transformer models for various NLP tasks.
• BERT: Bidirectional Encoder Representations from Transformers for NLP.

MLOps

• MLflow: Open-source platform to manage the ML lifecycle.
• Kubeflow: Machine Learning toolkit for Kubernetes.

Natural Language Processing (NLP)

• spaCy: Industrial-strength NLP library.
• NLTK: Natural Language Toolkit for working with human language data.

Usage

To use this cheatsheet, clone the repository and navigate through the folders to find the relevant library. Each markdown file contains detailed examples and usage instructions.

Clone the Repository

git clone https://github.com/mahdi-noori-ai/AI-libraries-cheatsheet.git
cd AI-libraries-cheatsheet

Navigate to Specific Library

cd [Category]/[Library].md

For example, to view the TensorFlow cheatsheet:

cd "AI Model Development"/TensorFlow.md

Folder Structure

AI-libraries-cheatsheet/
├── AI Model Development/
│   ├── TensorFlow.md
│   └── PyTorch.md
├── Computer Vision/
│   ├── OpenCV.md
│   └── scikit-image.md
├── Data Analysis and Manipulation/
│   ├── Pandas.md
│   └── NumPy.md
├── Data Visualization/
│   ├── Matplotlib.md
│   └── Seaborn.md
├── Deep Learning & Machine Learning/
│   ├── Keras.md
│   └── Scikit-learn.md
├── Generative Models/
│   ├── GANs.md
│   └── VAEs.md
├── LLMs/
│   ├── GPT.md
│   └── BERT.md
├── MLOps/
│   ├── MLflow.md
│   └── Kubeflow.md
├── NLP/
│   ├── spaCy.md
│   └── NLTK.md
└── README.md

Contributing

We welcome contributions from the community to enhance this resource. Please read the contribution guidelines for detailed instructions. You can also open an issue if you encounter any problems or have suggestions.

License

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

Acknowledgements

Special thanks to all contributors and the open-source community for their invaluable support and contributions.

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We hope you find this cheatsheet useful and it becomes an essential part of your AI development toolkit. Happy coding!