Thread by @svpino

25 popular libraries and frameworks for building machine and deep learning applications.

Covering:

▫️ Data analysis and processing
▫️ Visualizations
▫️ Computer Vision
▫️ Natural Language Processing
▫️ Reinforcement Learning
▫️ Optimization

A mega-thread.

🐍 🧵👇

(1 / 25) TensorFlow

TensorFlow is an end-to-end platform for machine learning. It has a comprehensive, flexible ecosystem of tools and libraries to build and deploy machine learning-powered applications.

(2 / 25) Keras

Keras is a highly-productive deep learning interface running on top of TensorFlow. It provides essential abstractions and building blocks for developing and shipping machine learning solutions with high iteration velocity.

(3 / 25) PyTorch

PyTorch is a machine and deep learning framework used primarily for natural language processing and computer vision applications. In the community, PyTorch has grown as a research-first library.

(4 / 25) FastAI

FastAI is a deep learning library providing high and low-level components to achieve state-of-the-art results in standard deep learning domains. FastAI sits on top of the PyTorch framework.

(5 / 25) Apache MXNet

MXNet is a lean, flexible and scalable deep learning framework suited for flexible research, prototyping, and production of deep learning applications.

(6 / 25) NumPy

NumPy is a tool for scientific computing and performing high-performance general-purpose array operations.

(7 / 25) Scikit-Learn

Scikit-Learn is a robust machine learning library featuring a wide range of supervised and unsupervised learning algorithms.

(8 / 25) Pandas

Pandas is a high-performance, easy-to-use library for processing and analyzing relational and structured data.

(9 / 25) Pandarallel

Padarallel is a library that allows the parallelization and speed up of computations when using pandas.

(10 / 25) SciPy

A library containing efficient mathematical routines as linear algebra, interpolation, optimization, integration, and statistics.

(11 / 25) OpenCV

A computer vision and machine learning library providing a common infrastructure for computer vision applications.

(12 / 25) Statsmodels

Statsmodels is a library providing easy computations for descriptive statistics and estimation and inference for statistical models. It also allows for conducting statistical tests and statistical data exploration.

(13 / 25) Matplotlib

A library designed for the exploration and visualization of data. It's the standard plotting library in Python.

(14 / 25) Seaborn

A data visualization library based on Matplotlib, providing a high-level interface for drawing graphics.

(15 / 25) Streamlit

Streamlit is a library that makes it easy to create and share beautiful, custom web applications for machine learning and data science.

(16 / 25) Pyevolve

Pyevolve is a complete genetic algorithm framework to solve optimization problems.

(17 / 25) PyBrain

Modular machine learning library containing algorithms for neural networks, reinforcement learning, unsupervised learning, and evolution.

(18 / 25) Open AI Gym

Gym is a toolkit for developing and comparing reinforcement learning algorithms.

(19 / 25) NLTK

NLTK is a suite of libraries for the symbolic and statistical processing of natural language. NLTK is used mainly to support the research and teaching of NLP.

(20 / 25) Stanza

Stanza is a natural language analysis package containing tools to process and analyze human languages. It brings state-of-the-art NLP pre-trained models supporting 66 languages.

(21 / 25) spaCy

spaCy specializes in processing and analyzing data in NLP, and it's built specifically for production use.

(22 / 25) Pattern

Pattern is a web mining module with NLP capabilities and machine learning model implementations.

(23 / 25) TextBlob

TextBlob provides an easy-to-use interface to the NLTK library, covering everyday NLP tasks ranging from parts-of-speech tagging to sentiment analysis and language translation to text classification.

(24 / 25) Gensim

Gensim is a topic modeling NLP library designed to process raw, unstructured digital texts using unsupervised machine learning algorithms.

(25 / 25) Transformers

Transformers is a high-performance library providing thousands of pre-trained models to perform natural language processing tasks in over 100+ languages.

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