NumPy — TheFoundation of
Scientific Computing in Python
Provides support for large, multi-dimensional arrays and matrices
Offers a wide range of mathematical functions to operate on these arrays
Enables fast vectorized operations, replacing slow Python loops
Core library for numerical computations in data science, machine learning, and
engineering
Integrates seamlessly with other Python libraries like Pandas and SciPy
2.
Pandas — PowerfulData
Manipulation and Analysis
Built on top of NumPy for efficient data handling
Provides two main data structures: Series (1D) and DataFrame (2D)
Ideal for cleaning, transforming, and exploring tabular data
Supports data import/export from CSV, Excel, SQL, and more
Widely used in data science for preparing datasets before modeling
3.
OpenCV — OpenSource Computer
Vision Library
Provides tools for image and video processing
Supports feature detection, object recognition, and tracking
Useful in robotics, augmented reality, and AI vision applications
Cross-platform and supports Python, C++, and Java
Integrates well with deep learning frameworks for advanced vision tasks
4.
PyTorch — DynamicDeep Learning
Framework
Developed by Facebook AI Research
Uses dynamic computation graphs for flexibility and ease of debugging
Supports GPU acceleration for fast training
Popular among researchers and practitioners for neural network development
Includes rich ecosystem: torchvision, torchaudio, and more
5.
TensorFlow — ScalableMachine
Learning Platform
Developed by Google Brain team
Supports static computation graphs optimized for production
Highly scalable across CPUs, GPUs, and TPUs
Offers TensorFlow Extended (TFX) for deployment and TensorFlow Lite for mobile
Large community and extensive tooling for model building and deployment
