1. TensorFlow
TensorFlow is an open-source machine learning framework developed by Google. It allows developers to build and train deep learning models efficiently.
Key Features:
- Flexible architecture for easy deployment on CPUs, GPUs, or TPUs
- Robust tools for model building and debugging
- Support for a wide range of platforms and devices
2. PyTorch
PyTorch is another popular machine learning library that offers dynamic computation graphs and seamless integration with Python.
Key Features:
- Intuitive API for easy model building and experimentation
- Support for GPU acceleration for faster training
- Active community and extensive documentation
3. Scikit-learn
Scikit-learn is a simple yet powerful library for machine learning in Python. It provides tools for data preprocessing, model selection, and evaluation.
Key Features:
- Easy-to-use interface for quick prototyping
- Support for a wide range of machine learning algorithms
- Integration with NumPy, SciPy, and Pandas for data manipulation
4. OpenCV
OpenCV is a computer vision library that offers a wide range of tools for image and video processing. It is widely used in AI applications such as object detection and facial recognition.
Key Features:
- Extensive collection of algorithms for image processing and computer vision tasks
- Support for multiple programming languages including Python, C++, and Java
- Optimized for real-time applications on various platforms
5. Jupyter Notebooks
Jupyter Notebooks is an interactive development environment that allows developers to create and share documents containing live code, equations, visualizations, and narrative text.
Key Features:
- Support for multiple programming languages including Python, R, and Julia
- Easy integration with data visualization libraries such as Matplotlib and Seaborn
- Collaboration features for sharing and publishing notebooks online
