With the rapid development of AI technology, various theories and practices emerge in an endless stream. It is rapidly changing almost every area of our lives from how we communicate to the means used for transportation. As a developer or learner, before choosing to build a machine learning application, choosing one from many open source projects should be a daunting task. A few days ago, there were netizens summarizing on the blog.8 best open source AI technologiesTo point the way for machine learning developers.
TensorFlow is a project created by Google to support its research and production goals. Launched in 2015, TensorFlow 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 and is currently used by a wide range of companies, including Dropbox, eBay, Intel, Twitter, and Uber.
Keras is an open source machine learning library, originally released in 2015, designed to simplify the creation of deep learning models. It is written in Python and can be deployed on other artificial intelligence technologies such as TensorFlow, Microsoft Cognitive Toolkit (CNTK) and Theano.
Keras is known for its user-friendliness, modularity, and extensibility. It enables simple and fast prototyping while supporting convolutional networks and loop networks, and it can run optimally on CPUs and GPUs.
Originally released in 2007, Scikit-learn is an open source library for machine learning. This traditional framework was written in Python. It is based on three other open source projects, Matplotlib, NumPy, and SciPy. It focuses on data mining and data. The analysis includes several machine learning models, including classification, regression, clustering, and dimensionality reduction.
4, Microsoft Cognitive Toolkit
The Microsoft Cognitive Toolkit, originally published in 2016, formerly known as CNTK, is an AI solution that allows you to take machine learning projects to a new level. Microsoft stated that the open source framework can "train deep learning algorithms to work like the human brain."
Some of the key features of the Microsoft Cognitive Toolkit include highly optimized components that can handle data from Python, C++, or BrainScript, provide efficient resource utilization, integrate easily with Microsoft Azure, and interoperate with NumPy.
Originally released in 2007, Theano is an open source Python library that allows developers to easily build various machine learning models. Because it is one of the earliest AI libraries, it is considered as an industry standard that promotes the development of deep learning.
The features of Theano are that it can simplify the process of defining, optimizing, and evaluating mathematical expressions. It can transform your data structure into very efficient code that integrates with native libraries such as NumPy, BLAS, and native code. In addition, it is optimized for the GPU and has extensive code testing capabilities.
The Caffe (Convolutional Architecture for Fast Feature Embedding), originally published in 2017, is a machine learning framework that focuses on expressiveness, speed, and modularity. The framework is written in C++ and comes with a Python interface.
Caffe's key features include inspiring innovative expressive architecture, promoting extensive code for aggressive development, accelerating the rapid performance of industry deployments, and stimulating growth in vibrant communities.
Originally released in 2002, Torch is a machine learning library that offers a wide range of deep learning algorithms. The framework provides developers with the flexibility and speed to optimize when dealing with machine learning projects. It is written using the scripting language Lua and comes with an underlying C implementation. Some of Torch's main features include N-dimensional arrays, linear algebra routines, numerical optimization routines, efficient GPU support, and support for the iOS and Android platforms.
Originally released in 2010, Accord.NET is a machine learning framework written entirely in C#. The framework is suitable for production-level scientific computing. With its extensive library, developers can build applications in areas such as artificial neural networks, statistical data processing, and image processing.