Programming languages utilize libraries to access a collection of functions about specific topics. Ideally, these libraries provide documentation explaining the functions they offer.
In my journey of learning Python, I’ve frequently encountered various libraries. However, it’s only through consistent use of these libraries that one can easily remember their purposes. With that in mind, here’s a small table highlighting the most important Python libraries for AI, along with links to their documentation:
| Library | Description | Documentation URL |
|---|---|---|
| TensorFlow | Open-source deep learning framework developed by Google Brain. | https://www.tensorflow.org/api_docs/python |
| PyTorch | Deep learning framework with dynamic computational graph, developed by Facebook’s AI Research lab. | https://pytorch.org/docs/stable/index.html |
| Scikit-learn | Python library for machine learning tasks including classification, regression, and clustering. | https://scikit-learn.org/stable/documentation.html |
| Keras | High-level neural networks API designed for easy and fast experimentation. | https://keras.io/api/ |
| Natural Language Toolkit | A platform for building Python programs to work with human language data. | https://www.nltk.org/api/nltk.html |
| Gensim | Library for topic modeling and document similarity analysis, focusing on unsupervised algorithms. | https://radimrehurek.com/gensim/auto_examples/index.html |
| OpenCV | Computer vision library offering functions for image and video analysis. | https://docs.opencv.org/master/ |
Carlos Peñafiel
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