How to build your own AI? A list of open source solutions
AI is the technological system of tomorrow; it develops quickly and penetrates deep into many spheres of humans’ lives. The market reflects this condition and as a result, offers a wide variety of open source solutions that help continue to build AI.
Here is a list of the most popular open source AI solutions:
TensorFlow is an open source machine learning framework. It was originally developed to support Google’s research. Nowadays, TensorFlow is the most widespread and well-maintained system for machine learning that is used by many popular companies such as Airbnb, Coca-Cola, eBay, Uber, Intel, Nvidia, Dropbox, Twitter.
Keras is a high-level neural networks API. It is written in Python and can run on top of TensorFlow, CNTK (Microsoft Cognitive Toolkit) or Theano.
Keras is simple to use, offering fast and hassle-free prototyping. Keras supports both convolutional and recurrent networks, and runs optimally on both CPUs (central processing units) and GPUs (graphics processing units).
Scikit-learn is an open source library developed for data mining and data analysis. It is accessible to everybody and reusable in various contexts.
Scikit-learn is written in Python and built on NumPy and SciPy.
Microsoft Cognitive Toolkit
Microsoft Cognitive Toolkit is a simple and efficient toolbox for machine learning. It is open-source, free and easy-to-use.
The key features of Microsoft Cognitive Toolkit are highly optimized, built-in components, memory sharing and other built-in methods to fit models in GPU memory, evaluation from Python, C++ and BrainScript.
Theano is an open source Python library that helps to create machine learning models.
The architecture allows integrating with NumPy, native libraries and native code. Theano has dynamic C code generation, and can detect and diagnose many different types of errors.
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework. It is open source, written in C++ and comes with a Python interface.
Caffe can boast expressive architecture, extensible code and speed that makes Caffe perfect for research.
Chainer is a Python-based, open source framework for deep learning models. It can be defined as a powerful, flexible and initiative system for Neural Networks.
Torch is an open source scientific computing framework. It is written in LuaJIT and an underlying C/CUDA implementation.
Torch helps to build extremely simple and fast scientific algorithms.
Apart from the above-listed AI solutions, there are other less popular technologies including: