Gensim is a Python library that automatically extracts semantic topics from documents, commonly used for topic modeling and document similarity tasks in NLP.

Gensim

Natural Language Processing is a process of manipulating or understanding speech or text with software or machine. NLTK contains libraries for statistical language processing. There are many NLP libraries out there for use but NLTK is by far the most diverse. Other NLP libraries are best suited for specific tasks.

NLTK

♢nltk Natural language toolkit, a natural language processing toolkit, is the most commonly used Python Library in the NLP field. ♢Gensim Python library that automatically extracts semantic topics from documents. ♢JieBa A powerful thesaurus, especially Chinese word segmentation.

University of Michigan - Ann Arbor

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- **NumPy**: Used for array operations and scientific/data computing.
- **Pandas**: Used for data manipulation, cleaning, and analysis via dataframes.
- **Matplotlib**: A core plotting library offering multiple data visualization options.
- **Seaborn**: Built on Matplotlib, used for creating attractive statistical graphics like heatmaps.
- **BeautifulSoup**: Used for web crawling and scraping data from HTML/XML files.
- **SciPy**: An open-source scientific computing library covering statistics, integration, optimization, and image processing.
- **StatsModels**: Provides classes and functions for estimating and testing various statistical models.

Data Science Libraries Within Python

♢Scikit-learn

Scikit-learn is a general machine learning algorithm library, which can realize common machine learning algorithms such as data preprocessing, classification, regression, dimension reduction, model selection and so on.

♢XGBoost / LightGMB

Boosting tree toolkit, in which xgboost is one of the two superstars of kaggle

Machine Learning Libraries Within Python

♢TensorFlow

Developed by Google brain team, the most mainstream deep learning framework has the best ecosystem. However, it is difficult to get started, the API is complex and changeable, and debugging is difficult.

♢PyTorch

Facebook deep learning library is oriented to academia, with simple model construction, easy debugging and excellent ecosystem. But it is not suitable for industrial product level applications.

♢MXNet

The lightweight, portable and flexible deep learning library developed by DMLC has good memory and video memory optimization, and has great potential in the future. Amazon and Baidu support. However, the official documents are not very detailed, and there are confusing parts.

♢Keras

Tensorflow's advanced API can also use theano or cntk as the backend. Focus on user friendliness, can quickly design and build models, and turn ideas into results. It is most suitable for novices. But it is the slowest, highly encapsulated and has poor flexibility.

♢Theano

Theano is the first widely adopted deep learning framework. At present, the development and maintenance of theano project has been terminated.

♢CNTK

Microsoft Research open source deep learning framework.

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