Artificial intelligence (AI) technology is rapidly changing almost every field of our lives. From how we communicate to the means of transportation, we seem to be more and more addicted to artificial intelligence. With the rapid development of AI, a large number of talents and resources are devoted to accelerating the development of technology. Using the best open source AI technology below, you can take your machine learning project to a new level.
TensorFlow, originally released in 2015, is an open source machine learning framework that is easy to use and deploy on various platforms. It is one of the best maintained and widely used frameworks in machine learning.
Google created to support its research and production goals, TensorFlow has been widely used by many companies, including Dropbox, eBay, Intel, Twitter and Uber. TensorFlow can be used in Python, C++, Haskell, Java, Go, Rust and Java. Of course, third-party software packages in other programming languages can also be found. The framework allows you to use flow charts to develop neural networks and even other computational models.
Keras is an open source software library originally released in 2015 to simplify the creation of in-depth learning models. It is written in Python and can be deployed on other AI technologies, such as TensorFlow, Microsoft Cognitive Toolkit (CNTK) and Theano.
Keras is known for its user friendliness, modularity and scalability. If you need a machine learning library, you can implement a simple and fast prototype design, support convolution network and cyclic neural network, and run on the CPU and GPU to the best state, which is very suitable.
Sckit-learning, originally released in 2007, is an open source library for machine learning. This traditional framework is written in Python and contains several machine learning models, including classification, regression, clustering and dimensionality reduction.
Scikit-learning is designed on three other open source projects, Matplotlib, NumPy and Scippy, which focus on data mining and data analysis.
4. Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit (formerly known as CNTK), originally released in 2016, is an AI solution that allows you to take machine learning projects to a new level.
Some important features of Microsoft Cognitive Toolkit include highly optimized components that handle data from Python, C++ or Brain, provide efficient resource usage, easy integration with Microsoft Azure and interoperability with NumPy.
Theano, originally released in 2007, is an open source Python library that allows you to easily build machine learning models. Because it is one of the oldest libraries, it is regarded as an industry standard to stimulate the development of in-depth learning.
Its core is to simplify the process of defining, optimizing and evaluating mathematical expressions. Theano can transform your structure into highly efficient code integrated with efficient native libraries such as NumPy, BLAS, and native code (C++).
In addition, it optimizes the GPU, provides efficient symbol differentiation, and has a wide range of code testing functions.
Cafe (Convolutional Architecture for Fast Feature Embedding), originally released in 2017, is a machine learning framework focused on expressiveness, speed and modularity. The open source framework is written in C++ with a Python interface.
Caffe's main features include an expressive architecture that encourages innovation, a wide range of code that contributes to positive development, accelerates rapid performance of industry deployments, and a vibrant community that promotes growth.
Torch, originally released in 2002, is a machine learning library that provides a wide range of in-depth learning algorithms. Open source frameworks provide you with the flexibility and speed to optimize when dealing with machine learning projects without creating unnecessary complexity in the process.
It is written in scripting language Lua with an underlying C implementation. Some of Torch's main functions include N-dimensional arrays, linear algebraic routines, numerical optimization routines, efficient GPU support, and support for iOS and Android platforms.
Accord. NET, originally released in 2010, is a machine learning framework written entirely in C#. The open source framework is suitable for scientific computing at the production level. With its extensive database, you can build a variety of applications in artificial neural networks, statistical data processing, image processing and so on.
Before starting to build machine learning applications, choosing a technology from a wide range of options can be a daunting task. Therefore, it is important to evaluate several options before making a final decision. In addition, learning how machine learning techniques work can help you make the right decisions.