Machine learning libraries are essential tools for developers and researchers to build and deploy machine learning models. Below are some popular machine learning libraries that you can explore.
Popular Machine Learning Libraries
TensorFlow: An open-source library developed by Google Brain team for dataflow and differentiable programming across a range of tasks.
PyTorch: An open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.
Scikit-learn: A Python-based library for machine learning in Python which features various classification, regression and clustering algorithms.
Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
Image Processing Libraries
OpenCV: Open Source Computer Vision Library, which is used for various image processing tasks.
- OpenCV
Pillow: A Python Imaging Library fork that adds some user friendly features.
- Pillow
Scikit-image: A collection of algorithms for image processing and computer vision.
Natural Language Processing Libraries
NLTK: The Natural Language Toolkit, or NLTK, is a leading platform for building Python programs to work with human language data.
- NLTK
spaCy: An industrial-strength natural language processing library that offers advanced NLP features.
- spaCy
gensim: Topics modeling for documents.
Data Science Libraries
Pandas: A powerful Python library for data manipulation and analysis.
- Pandas
NumPy: A fundamental package for scientific computing with Python.
- NumPy
Matplotlib: A comprehensive library for creating static, animated, and interactive visualizations in Python.
By exploring these libraries, you can enhance your machine learning capabilities and build powerful models.