Artificial intelligence is an exceptional technology following the futuristic approach. In this progressive era, it’s capturing the attention of all the multination organizations. Some of the popular names in the industry like Google, IBM, Facebook, Amazon, Microsoft constantly investing in this new-age technology.
Anticipate in business needs using artificial intelligence and take research and development on another level. This advanced technology is becoming an integral part of organizations in research and development offering ultra-intelligent solutions. It helps you maintain accuracy and increase productivity with better results.
AI open source tools and technologies are capturing the attention of every industry providing with frequent and accurate results. These tools help you analyse your performance while providing you with a boost to generate greater revenue.
Without further ado, here we have listed some of the best open-source tools to help you understand artificial intelligence better.
TensorFlow is an open-source machine learning framework used for Artificial Intelligence. It is basically developed to conduct machine learning and deep learning for research and production. TensorFlow allows developers to create dataflow graphics structure, It moves through a network or a system node, and the graph provides a multidimensional array or tensor of data.
TensorFlow is an exceptional tool that offers countless advantages.
- Simplifies the numeric computation
- TensorFlow offers flexibility on multiple models.
- TensorFlow improves business efficiency
- Highly portable
- Automatic differentiate capabilities.
2. Apache SystemML
Apache SystemML is a very popular open-source machine learning platform created by IBM offering a favourable workplace using big data. It can run efficiently and on Apache Spark and automatically scale your data while determining whether your code can run on the drive or Apache Spark Cluster. Not just that, its lucrative features make it stand out in the industry offers;
- Algorithms customization
- Multiple Execution Modes
- Automatic Optimisation
It also supports deep learning while enabling developers to implement machine learning code and optimizing it with more effectiveness.
OpenNN is an open-source artificial intelligence neural network library for progressive analytics. It helps you develop robust models with C++ and Python while containing algorithms and utilities to deal with machine learning solutions likes forecasting and classification. It also covers regression and association providing high performance and technology evolution in the industry.
It possesses numerous lucrative features like;
- Digital Assistance
- Predictive Analysis
- Fast Performance
- Virtual Personal Assistance
- Speech Recognition
- Advanced Analytics
It helps you design advance solutions implementing data mining methods for fruitful results.
Caffe (Convolutional Architecture for Fast Feature Embedding) is an open-source deep learning framework. It considers speed, modularity, and expressions the most. Caffe was originally developed at the University of California, Berkeley Vision and Learning Centre, written in C++ with a python interface. It smoothly works on operating system Linux, macOS, and Windows.
Some of the key features of Caffe that helps in AI technology.
- Expressive Architecture
- Extensive Code
- Large Community
- Active Development
- Speedy Performance
It helps you inspire innovation while introducing stimulated growth. Make full use of this tool to get desired results.
Torch is an open-source machine learning library which, helps you simplify complex task like serialization, object-oriented programming by offering multiple convenient functions. It offers the utmost flexibility and speed in machine learning projects. Torch is written using scripting language Lua and comes with an underlying C implementation. It is used in multiple organization and research labs.
Torch has countless advantages like;
- Fast & Effective GPU Support
- Linear algebra Routines
- Support for iOS & Android Platform
- Numeric Optimization Routine
- N-dimensional arrays