Welcome to AI Python Libraries

PyTensor is a Python library that allows you to define, optimize/rewrite, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.

is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

The RAPIDS suite of open-source software libraries executes end-to-end data science and analytics pipelines entirely on GPUs.

H2O is a machine learning and predictive analytics platform that enables the construction of machine learning models on big data.

FLAML is a lightweight Python library that automatically identifies accurate machine learning models. It selects learners and hyperparameters automatically, saving machine learning practitioners considerable time and effort.

PyTorch Lightning offers a high-level interface for PyTorch. Its high-performance and lightweight framework can organize PyTorch code to decouple the research from the engineering, making deep learning experiments simpler to understand and reproduce. It was developed to create scalable deep learning models that can seamlessly run on distributed hardware.

Hugging Face Transformers is an open-source library by Hugging Face. Transformers allow APIs to easily download and train state-of-the-art pre-trained models. Using pre-trained models can reduce your compute costs, carbon footprint, and save you time from having to train a model from scratch.

Polars is a high-performance DataFrame library optimized for large data sets. It utilizes lazy evaluation and multi-threading for rapid data operations.

Pydot is an interface to Graphviz and is ideal for generating complex graphs.

Although inactive, PyBrain offers a range of machine-learning algorithms. It was designed for both beginners and advanced users.

Built on Keras and Apache Spark, Dist-Keras focuses on distributed deep learning.

Developed by BVLC and BAIR, Caffe specializes in vision-based machine learning tasks. It excels in image classification and convolutional neural networks.

Fuel acts as a data pipeline for machine learning models, offering out-of-the-box support for popular datasets and on-the-fly data preprocessors.

Scrapy is a robust framework for scraping structured data from the web, supporting large-scale data extraction.

Pattern is an all-in-one solution offering machine learning algorithms, data collection, and analysis. It supports data mining from sources like Google, Twitter, and Wikipedia.

This library was developed at Stanford University and written in Java. It has been pivotal in academic and research settings due to its accurate natural language parsing and rich linguistic annotations.

For computer vision tasks like recognizing faces or objects in images, OpenCV is a go-to library. It has thousands of algorithms for working with images and videos. It's used in different fields, from academics to industry.

The Hugging Face is an open-source library that provides a vast array of pre-trained models primarily focused on NLP.

LangChain is a framework for developing applications powered by large language models (LLMs). LangChain simplifies every stage of the LLM application lifecycle.

Beautiful Soup is a Python library for pulling data out of HTML and XML files. It works with your favorite parser to provide idiomatic ways of navigating, searching, and modifying the parse tree. It commonly saves programmers hours or days of work.

OpenAI provides access to one of the most powerful AI models for natural language processing.

Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries.

Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models

LlamaIndex is a framework for building context-augmented generative AI applications with LLMs including agents and workflows.

Pinecone is the leading AI infrastructure for building accurate, secure, and scalable AI applications. Use Pinecone Database to store and search vector data at scale, or start with Pinecone Assistant to get a RAG application running in minutes.

A large language model startup offering access to powerful AI models through an API.

FastAPI is a modern, fast (high-performance), web framework for building APIs with Python based on standard Python type hints.

A staple in the data science community, Jupyter Notebooks facilitate the sharing of live code, visualizations, and narrative text, making them an invaluable tool for prototyping and experimentation.

This platform is crucial for teams working on generative AI projects, offering functionalities for tracking experiments, version controlling machine learning models, and generating collaborative reports.

Fast Artificial Neural Network Library, or FANN, implements artificial neural networks in C (which is what makes it up to 150 times faster than other libraries) while making them accessible in a number of different languages, including Python.

ffnet is a Python AI library for implementing feed-forward neural networks. It uses a graphical user interface to visualize training datasets.

Where OpenCV focuses on comprehensiveness and customizability, SimpleCV focuses on making computer vision easy. The learning curve is much smaller to the point where getting images from a camera is as simple as initializing a camera (using Camera()) and getting its image (using Camera.getImage()). This Python AI library is a stellar choice for developers focused on common computer vision applications as opposed to highly customized solutions.

PyCLIPS provides an inference engine to Python applications. It provides a rules-based engine as binary modules inside the library that are accessed using classes and functions. The engine itself stays “alive” in a separate memory space to the Python space, so inferences and rules are persisted as your program grows in functionality.

Also inspired by CLIPS, Experta is a rule engine that pairs a set of facts with a set of rules based on those facts. Then, actions are executed based on these rules. All facts and rules are held by the implemented knowledge engine which determines the expert output of the system when it is called.

A flexible and efficient deep learning framework designed for both efficiency and productivity.

An inference engine designed to run ONNX models efficiently across platforms.

Data validation and settings management using Python type annotations.

A visual analysis and diagnostic tool for machine learning models.

A library for wavelet transforms in Python.

An open-source data visualization and analysis tool for both novice and expert users.

A high-performance distributed execution framework that makes it easy to scale applications across clusters.  

A distributed deep learning training framework that makes it easy to scale TensorFlow and PyTorch models.  

A set of Python modules designed for writing video games, useful for AI in game development.  

A library for probabilistic reasoning and statistical analysis in TensorFlow.  

A set of reliable implementations of reinforcement learning algorithms based on PyTorch.  

An open-source machine learning framework for building contextual AI assistants and chatbots.  

An open-source NLP research library built on PyTorch focused on deep learning models for NLP tasks.  

A sequence-to-sequence learning toolkit for training custom models on various tasks in NLP.     

An open-source library for building conversational AI systems with various pre-trained models.     

A library for efficient text classification and representation learning developed by Facebook AI Research.      

A library for parsing HTML and XML documents, useful in web scraping.