NumPy: The cornerstone of numerical computing in Python. Provides efficient arrays, linear algebra operations, and architectural mathematical functions essential for most AI applications.
SciPy: Builds on NumPy, offering advanced algorithms for scientific computing, optimization, integration, and statistics. Useful for data pre-processing and analysis tasks.
pandas: A data manipulation and analysis workhorse. Enables working with labeled data in Data Frames, data cleaning, and exploratory data analysis.
Scikit-learn (sklearn): A comprehensive library for traditional machine learning algorithms. Includes classification, regression, clustering, dimensionality reduction, and model selection tools. Great for getting started with machine learning concepts and building baseline models.
TensorFlow: A powerful open-source library developed by Google for numerical computation using data flow graphs. Used for building and training deep learning models. Offers flexibility and customization.
PyTorch: Another popular deep learning library, known for its ease of use, dynamic computational graphs, and extensive support for research and development. Often favored for rapid prototyping and experimentation.
Keras: A high-level neural network API that can run on top of TensorFlow, PyTorch, or other backends. Simplifies the process of building and training neural networks by providing a user-friendly interface.
Matplotlib/Seaborn: Essential for data visualization, creating various plots and charts to understand your data and model performance.
Jupyter Notebook/Lab: Interactive environments for writing and running Python code, visualizing data, and documenting your work.
OpenAI Gym: A toolkit for developing and testing reinforcement learning agents in simulated environments.
Hugging Face Transformers: Provides pre-trained models for natural language processing (NLP) tasks like text classification, question answering, and text generation.
The term Artificial Intelligence (AI) is often used without much thought about what is actually being said. People are now using it to refer to most anything done by computer even if it is relatively simple. In your list, except for Scikit-learn, Keras, OpenAI Gym and Hugging Face Transformer, I see no direct relevance of that software to AI. Most of them say they are useful for AI but not that they are AI.
AI is not something specific. It is a general term to refer to a few specific things, most of which are:
- Natural Language is the recognition of languages that people use
- An expert system is the type of thing that Chat-GPT is famous for. An expert system consists of a knowledge base and an inference engine to process the knowledge
- Neural networks
- Pattern recognition
I suggest you first refine your definition of Libraries for AI development (such as whether the library is directly or indirectly relevant) and sub-divide them into the categories of AI.