Artificial intelligence (AI) technology is rapidly changing almost every area of ​​our lives. From how we communicate to the means of transportation, we seem to be more and more addicted to artificial intelligence. Due to the rapid development of AI, a large number of talents and resources are committed to accelerating the development of technology. Take advantage of the best open source AI technology below to take your machine learning project to the next level.
1.TensorFlow
Originally released in 2015, TensorFlow is an open source machine learning framework that is easy to use and deploy on a variety of platforms. It is one of the best-maintained and widely used frameworks for machine learning.
Google has been created to support its research and production goals, and TensorFlow is now widely used by companies including Dropbox, eBay, Intel, Twitter and Uber. TensorFlow is available for Python, C++, Haskell, Java, Go, Rust, and Java. Of course, you can also find third-party packages for other programming languages. This framework allows you to use flowcharts to develop neural networks, even other computing models.
2.Keras
Keras is an open source software library originally released in 2015 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 scalability. If you need a machine learning library, you can achieve simple and rapid prototyping, support both convolutional networks and recurrent neural networks, and run optimally on the CPU and GPU, which is very suitable.
3.scikit-learn
Originally released in 2007, scikit-learn is an open source library developed for machine learning. This traditional framework is written in Python and includes several machine learning models, including classification, regression, clustering, and dimensionality reduction.
Scikit-learn is designed on three other open source projects, Matplotlib, NumPy and SciPy, which focus on data mining and data analysis.
4. Microsoft CogniTIve Toolkit
Originally released in 2016, the Microsoft Cognitive Toolkit (formerly known as CNTK) is an AI solution that takes robot learning to the next level.
Some of the key features of the Microsoft CogniTIve Toolkit include highly optimized components that can process data from Python, C++ or Brain, provide efficient resource usage, easily integrate with Microsoft Azure, and interoperate with NumPy.
5.Theano
Originally released in 2007, Theano is an open source Python library that allows you to easily build a variety of machine learning models. Since it is one of the oldest libraries, it is seen as an industry standard that spurs the development of deep learning.
At its core is the process of simplifying the definition, optimization, and evaluation of mathematical expressions. Theano converts your structure into very efficient code that integrates with efficient native libraries such as NumPy, BLAS, and native code (C++).
In addition, it is optimized for GPUs, provides efficient symbol differentiation, and has extensive code testing capabilities.
6.Caffe
Originally released in 2017, Caffe (a convolutional architecture for fast feature embedding) is a machine learning framework that focuses on expressiveness, speed and modularity. The open source framework is written in C++ and comes with a Python interface.
Key features of Caffe include an expressive architecture that encourages innovation, extensive code that contributes to positive development, accelerates the rapid performance of industry deployments, and a vibrant community to drive growth.
7.Torch
Originally released in 2002, Torch is a machine learning library that provides a wide range of deep learning algorithms. The open source framework gives you the flexibility and speed of optimization when dealing with machine learning projects without creating unnecessary complexity in the process.
It is written in 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 iOS and Android platforms.
8.Accord.NET
Originally released in 2010, Accord.NET is a machine learning framework written entirely in C#. The open source framework is suitable for production-level scientific computing. With its extensive library, you can build applications in artificial neural networks, statistical data processing, image processing, and more.
in conclusion
Choosing one of many options can be a daunting task before you start building machine learning applications. Therefore, it is important to evaluate several options before making a final decision. In addition, learning how various machine learning techniques work can help you make the right decisions.
PET is a milky white or light yellow highly crystalline polymer with a smooth and shiny surface. Good creep resistance, fatigue resistance, friction resistance and dimensional stability, small wear and high hardness, with the largest toughness of thermoplastics: good electrical insulation performance, little affected by temperature, but poor corona resistance. Non-toxic, climate resistance, chemical resistance, good stability, low water absorption, weak acid and organic solvent resistance, but not heat - resistant water immersion, alkali resistance.
China Membrane Switch, Membrane Switch Manufacturer, Membrane Switch, PET Membrane Switch
KEDA MEMBRANE TECHNOLOGY CO., LTD , https://www.kedamembrane.com