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Purpose: Open-source machine learning library for Python. Key Feature: Simple and efficient tools for data mining and data analysis.

Purpose: Open-source software library for advanced natural language processing in Python. Key Feature: Offers robust syntactic analysis, tokenization, and entity recognition.

Purpose: Deep contextualized word representation that models both complex characteristics of word use and how these uses vary across linguistic contexts. Key Feature: Generates embeddings for words that are function of the entire input sentence.

Purpose: Open-source library for numerical computation and machine learning. Key Feature: Automatic differentiation for implementing machine learning algorithms.

Purpose: Toolkit for developing and comparing reinforcement learning algorithms. Key Feature: Provides a wide variety of environments to test and benchmark algorithms.

Purpose: Python library that allows you to define, optimize, and evaluate mathematical expressions. Key Feature: Tight integration with NumPy and transparent use of GPU.

Purpose: Deep learning framework made with expression, speed, and modularity in mind. Key Feature: Its design allows for easy switching between CPU and GPU.

Purpose: Library for natural language processing that provides general-purpose architectures for natural language understanding and generation. Key Feature: Provides thousands of pre-trained models in 100+ languages.

Purpose: Open-source software library that provides a Python interface for artificial neural networks. Key Feature: User-friendly interface that enables quick prototyping of deep learning models.

Purpose: Open-source software library providing a gradient boosting framework for C++, Java, Python, R, and Julia. Key Feature: Designed for speed and performance.

Purpose: Open-source library for deep learning that simplifies the process of training fast and accurate neural nets. Key Feature: Provides one of the easiest to use frameworks to get started with deep learning.

Purpose: Python library used for topic modeling, document indexing, and other natural language processing tasks. Key Feature: Specializes in unsupervised semantic modeling from plain text.

Purpose: Gradient boosting framework that uses tree-based learning algorithms. Key Feature: It is designed for distributed and efficient training.

Purpose: Open-source machine learning library with a focus on deep learning. Key Feature: Dynamic computation graphs that allow for flexible model building and modification.