Tools

This page lists tools and libraries for building, training, and deploying machine learning models.
We follow a dual track approach:

• Python is the current focus of this tutorial, thanks to its simplicity and vast ecosystem.
• Rust will be introduced later, offering performance, safety, and modern systems programming advantages.

Both ecosystems are valuable. As you progress, you’ll see how ideas map across Python and Rust.

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🐍 Python Tools

Python is the de facto language for AI/ML. It has an extensive ecosystem that supports everything from data preprocessing to large-scale deep learning.

• Python Programming Language

  • Purpose: High-level, beginner-friendly language.
  • Explanation: Powers rapid experimentation and ML research.
  • Open-Source: python.org

• NumPy

  • Purpose: Numerical computing.
  • Explanation: Provides arrays, matrices, and vectorized math operations.
  • Open-Source: github.com/numpy/numpy

• Pandas

  • Purpose: Data manipulation and analysis.
  • Explanation: Intuitive DataFrame structures for handling datasets.
  • Open-Source: github.com/pandas-dev/pandas

• scikit-learn

  • Purpose: Traditional ML algorithms.
  • Explanation: Regression, classification, clustering, and preprocessing tools.
  • Open-Source: github.com/scikit-learn/scikit-learn

• PyTorch

  • Purpose: Deep learning framework.
  • Explanation: Dynamic computation graphs, widely used in academia and industry.
  • Open-Source: github.com/pytorch/pytorch

• Transformers (Hugging Face)

  • Purpose: State-of-the-art NLP models.
  • Explanation: Pretrained models for text classification, translation, and more.
  • Open-Source: github.com/huggingface/transformers

• FastAPI

  • Purpose: Deploy ML models as web APIs.
  • Explanation: Fast, modern Python web framework.
  • Open-Source: github.com/tiangolo/fastapi

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🦀 Rust Tools

Rust is emerging as a strong language for ML, with a rapidly growing ecosystem. It emphasizes performance and memory safety, making it well-suited for production systems.

• Rust Programming Language

  • Purpose: Systems language for ML tasks.
  • Explanation: Ensures speed and safety with zero-cost abstractions.
  • Open-Source: github.com/rust-lang/rust

• linfa

  • Purpose: Traditional ML algorithms.
  • Explanation: Regression, clustering, and other core ML tasks.
  • Open-Source: github.com/rust-ml/linfa

• tch-rs

  • Purpose: Deep learning with PyTorch C++ backend.
  • Explanation: Rust bindings for PyTorch.
  • Open-Source: github.com/LaurentMazare/tch-rs

• rust-bert

  • Purpose: NLP tasks.
  • Explanation: Pretrained models for sentiment analysis, translation, etc.
  • Open-Source: github.com/guillaume-be/rust-bert

• polars

  • Purpose: Data processing.
  • Explanation: High-performance DataFrame library.
  • Open-Source: github.com/pola-rs/polars

• nalgebra

  • Purpose: Linear algebra computations.
  • Explanation: Matrices and vectors for ML/math foundations.
  • Open-Source: github.com/dimforge/nalgebra

• actix-web

  • Purpose: Model deployment.
  • Explanation: Web framework for serving Rust ML models as APIs.
  • Open-Source: github.com/actix/actix-web

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Next Steps

• Learn more about Why Python for AI/ML.
• Or check Why Rust for AI/ML.
• Continue to Tutorial Roadmap.