Welcome to the guide on popular Machine Learning Libraries! These tools are essential for developers and data scientists to build, train, and deploy ML models efficiently. Here's a quick overview:

🚀 Top ML Libraries

  • TensorFlow
    A开源库 developed by Google, ideal for research and production.

    TensorFlow
    [Explore TensorFlow tutorials](/en/resources/tutorials/tensorflow)

  • PyTorch
    Known for its flexibility and dynamic computation graphs.

    PyTorch
    [Check PyTorch documentation](/en/resources/docs/pytorch)

  • Scikit-learn
    A Python library for classical ML algorithms and data preprocessing.

    Scikit-learn
    [Learn more about Scikit-learn](/en/resources/guides/data_preprocessing)

  • Keras
    A high-level API for rapid prototyping, often used with TensorFlow.

    Keras
    [Keras official website](https://keras.io)

📊 Key Features

Library Use Case Language
TensorFlow Research, production Python
PyTorch Deep learning, NLP Python
Scikit-learn Data science, ML basics Python
XGBoost Gradient boosting C++/Python
LightGBM Gradient boosting C++/Python
ONNX Model interoperability C++/Python
Theano Deep learning (legacy) Python
Caffe CNNs, computer vision C++

🧠 Why Choose These Libraries?

  • TensorFlow_Lite for mobile and embedded devices
    TensorFlow_Lite
  • ONNX_Runtime for fast model inference
    ONNX_Runtime
  • Theano for symbolic math and GPU acceleration

For deeper insights, visit our ML Tools Comparison Page.