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Cheat Sheets Collection

Welcome to the cheat sheets collection - a comprehensive set of quick reference guides for various programming languages, frameworks, and tools.

Getting Started

  • README - Overview and contribution guidelines

Available Cheat Sheets

Python

  • Inquirer - Interactive command-line prompts and user interfaces
  • Keras - High-level neural networks API with multi-backend support
  • LangChain - Framework for building LLM-powered applications
  • LangExtract - Google's language detection and information extraction
  • Matplotlib - Core plotting and data visualization library
  • NLTK - Natural Language Toolkit for text processing and analysis
  • NumPy - Numerical computing basics and advanced operations
  • Pandas - Data manipulation and analysis fundamentals
  • Pillow - Python Image Library for image processing
  • Polars - Blazingly fast DataFrame library with lazy evaluation and powerful expressions
  • Python - Core Python language syntax, data structures, and built-in features
  • PyTorch - Deep learning framework for tensors, neural networks, and training
  • Scikit-learn - Machine learning workflows, algorithms, and model evaluation
  • SciPy - Scientific computing algorithms and mathematical functions
  • Seaborn - Statistical data visualization with elegant defaults
  • Sentence-Transformers - Semantic similarity and text embeddings
  • TensorFlow - Machine learning platform with Keras integration
  • TorchVision - Computer vision utilities and pre-trained models
  • Transformers - Hugging Face transformers for modern NLP

JavaScript & Frameworks

  • React - Component-based UI library with hooks, state management, and modern patterns
  • Next.js - Full-stack React framework with App Router, SSR/SSG, and API routes

Machine Learning & Data Science

GPU Computing

  • CUDA - Comprehensive CUDA C/C++ programming reference for GPU computing

Operating Systems

  • Bottlerocket OS - Container-optimized Linux OS with AWS SSM, containerd management, and security best practices

Tools & Editors

Contributing

We welcome contributions! Please see the README for guidelines on adding new cheat sheets or improving existing ones.

Each cheat sheet should include: - Basic syntax and common patterns - Frequently used operations - Advanced but commonly needed functionality - Practical examples with clear explanations

Organization

Cheat sheets are organized by topic: - python/ - Python-related libraries and tools - javascript/ - JavaScript frameworks and libraries - machine-learning-algorithms.md - General machine learning and data science concepts - gpu/ - GPU computing and parallel programming - os/ - Operating systems and system administration - tools/ - Development tools and editors - Additional directories can be added for other languages/topics as needed

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