1. TensorFlow: TensorFlow™ is an open source software library for high-performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters of servers to mobile and edge devices. Originally developed by researchers and engineers from the Google Brain team within Google’s AI organization, it comes with strong support for machine learning and deep learning and the flexible numerical computation core is used across many other scientific domains. (Source: https://www.tensorflow.org)
2. Scikit-Learn: Scikit-learn is a machine learning library available for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. (Source: Wikipedia)
3. PyTorch: PyTorch is a deep learning framework for fast, flexible experimentation.
PyTorch is a python package that provides two high-level features:
- Tensor computation (like numpy) with strong GPU acceleration
- Deep Neural Networks built on a tape-based autodiff system (Source: https://pytorch.org/about/)
4. Gensim : Gensim is a robust open-source vector space modeling and topic modeling toolkit implemented in Python. It uses NumPy, SciPy and optionally Cython for performance. Gensim is specifically designed to handle large text collections, using data streaming and efficient incremental algorithms, which differentiates it from most other scientific software packages that only target batch and in-memory processing. (Source: Wikipedia)
5. Shogun: Shogun is and open-source machine learning library that offers a wide range of efficient and unified machine learning methods. (Source: http://shogun-toolbox.org)
6. Keras: Keras is an open source neural network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or MXNet. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible. It was developed as part of the research effort of project ONEIROS (Open-ended Neuro-Electronic Intelligent Robot Operating System), and its primary author and maintainer is François Chollet, a Google engineer. (Source: Wikipedia)
7. Caffe: CAFFE (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at UC Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface.
8. Statsmodels: Statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. The online documentation is hosted at statsmodels.org. (Source: Statsmodels.org)
9. Theano: Theano is a numerical computation library for Python. In Theano, computations are expressed using a NumPy-esque syntax and compiled to run efficiently on either CPU or GPU architectures.
Theano is an open source project primarily developed by a machine learning group at the Université de Montréal. (Source: Wikipedia)
10. NuPIC: (NuPIC) Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex. (Source: Github)
11. NuPIC: Nilearn is a Python module for fast and easy statistical learning on NeuroImaging data.
It leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis. (Source: https://nilearn.github.io)
12. Pymc: PyMC is a python module that implements Bayesian statistical models and fitting algorithms, including Markov chain Monte Carlo. Its flexibility and extensibility make it applicable to a large suite of problems. Along with core sampling functionality, PyMC includes methods for summarizing output, plotting, goodness-of-fit and convergence diagnostics. (Source: https://pymc-devs.github.io)
13. Microsoft Cognitive Toolkit: The Microsoft Cognitive Toolkit—previously known as CNTK—empowers you to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed, and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms you already use. (Source: Microsoft)
14. PredictionIO: Apache PredictionIO® is an open source Machine Learning Server built on top of a state-of-the-art open source stack for developers and data scientists to create predictive engines for any machine learning task. It lets you:
- quickly build and deploy an engine as a web service on production with customizable templates;
- respond to dynamic queries in real-time once deployed as a web service;
- evaluate and tune multiple engine variants systematically; (Source: PredictionIO)
15. Aerosolve: Aerosolve is created by Airbnb. Aerosolve provides sophisticated machine learning features, such as geo-based features, controllable quantization and feature interaction. Provide human intuition to machine models by specifying prior beliefs. (Source: Aerosolve)
Note: Content is taken from different sources including wikipedia, github etc. It is been mentioned in every section)
Asif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
