Artificial intelligence is a remarkable technology that follows the path of the future. In this age of continuous development, it attracts the attention of all transnational organizations. Google, IBM, Facebook, Amazon, Microsoft and other big names in the industry continue to invest in this new-age technology.
Predicting business needs requires the use of ARTIFICIAL intelligence and research and development at another level. This advanced technology is becoming an integral part of r&d organizations that provide ultra-intelligent solutions. It can help you maintain accuracy and increase productivity with better results.
AI open source tools and technologies have captured the attention of every industry with frequent and accurate results. These tools can help you analyze your performance and bring you greater returns.
Needless to say, here’s a list of some of the best open source tools to help you better understand AI.
1, TensorFlow
TensorFlow is an open source machine learning framework for artificial intelligence. It was developed primarily for machine learning and deep learning research and production. TensorFlow allows developers to create graph structures of data flows as they move through network or system nodes. Graphs provide multidimensional arrays or tensors of data.
TensorFlow is an excellent tool with countless advantages.
- Simplified numerical calculation
- TensorFlow provides flexibility over multiple models.
- TensorFlow improves business efficiency
- Highly portable
- Automatic discrimination ability
2, Apache SystemML
Apache SystemML is a very popular open source machine learning platform created by IBM that provides a good platform for using big data. It runs efficiently on Apache Spark and automatically scales the data while determining whether code can run on disk or on an Apache Spark cluster. Not only that, its rich functions make it stand out in the industry products;
- Algorithm customization
- Multiple execution modes
- Automatic optimization
It also supports deep learning, allowing developers to implement machine learning code and optimize it more efficiently.
3, OpenNN
OpenNN is an open source ARTIFICIAL intelligence neural network library for progressive analysis. It helps you develop robust models using C++ and Python, and it contains algorithms and programs for handling machine learning solutions such as prediction and classification. It also covers regression and correlation to provide industry performance and technology evolution.
It has rich functions, such as:
- Digital assistance
- Forecast analysis
- Fast performance
- Virtual personal assistance
- Speech recognition
- Advanced analysis
It can help you design advanced solutions to implement data mining and achieve fruitful results.
4, Caffe
Caffe (Convolution Architecture for Fast Feature Embedding) is an open source deep learning framework. It prioritizes speed, modularity, and expressions. Caffe was originally developed at the university of California, Berkeley’s center for vision and learning and is written in C++ with a Python interface. It runs smoothly on Linux, macOS, and Windows.
Caffe has some key features that contribute to AI technology.
- Expressive structure
- Extensible code
- A large community
- The development of active
- Performance fast
It can help you stimulate innovation and stimulate growth. Take full advantage of this tool to get the results you want.
5, the Torch
Torch is an open source machine learning library that helps you simplify complex tasks like serialization and object-oriented programming by providing a variety of convenient features. It offers maximum flexibility and speed in machine learning projects. Torch is written in the scripting language Lua, with the underlying implementation in C. It is used in multiple organizations and research laboratories.
Torch has numerous advantages, such as:
- Fast and efficient GPU support
- Linear algebra subroutine
- Supports iOS and Android platforms
- Numerical optimization subroutine
- N array
6, Accord the.net
Accord.NET is one of the famous free and open source AI development tools. It has a set of libraries for combining audio and image processing libraries written in C#. From computer vision to computer hearing, signal processing and statistical applications, it can help you build everything for business use. It comes with a comprehensive set of sample applications to quickly run various libraries.
You can develop an advanced application using Accord.net’s impressive features, such as:
- Statistical analysis
- Data access
- The adaptive
- Deep learning
- Second order neural network learning algorithm
- Digital assistance and multilingualism
- Speech recognition
7, Scikit – Learn
Scikit-learn is one of the popular open source tools to aid AI technology. It is a valuable library in Python for machine learning. It includes efficient tools such as machine learning and statistical modeling, including classification, clustering, regression, and dimensionality reduction.
Let’s Learn more about sciKit-Learn:
- Cross validation
- Clustering and classification
- Manifold learning
- Machine learning
- Virtual process automation
- Workflow automation
Scikit-learn helps you handle everything from pre-processing to model selection. It simplifies all tasks from data mining to data analysis.
conclusion
These are some of the popular open source AI tools that offer comprehensive functionality. Before developing a new-age app, one must choose one of these tools and do the work accordingly. These tools provide advanced AI solutions and keep up with the latest trends.
Artificial intelligence is applied on a global scale, everywhere. With the help of Amazon Alexa, Siri and other apps, AI provides customers with a good user experience. It has a significant advantage in industries that attract users’ attention. In all industries — healthcare, banking, finance, e-commerce — AI has saved a lot of time and effort while promoting growth and productivity.
Choose any of these open source tools for a better user experience and incredible results. It will help you grow and get better results in terms of quality and safety.
Via: opensourceforu.com/2019/11/7-b…
By Nitin Garg (lujun9972
This article is originally compiled by LCTT and released in Linux China