SimpleSysAdmin.info. Machine learning is a method of data analysis that automates analytical model building that gives computers the ability to learn without being explicitly programmed. Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data.
Here the following tools for Artificial Intelligence and Machine Learning, part 2.
Please read the part 1 first before you read this article.
- Apache Spark MLlib
MLlib is the machine learning library that is furnished with Apache Spark, the in memory group based open source data processing system. It includes a vast database of algorithms concentrating on classification, regression, clustering and collaborative filtering.
It is intended for straightforwardness, scalability, and simple integration with different tools. With the scalability, language comparability, and speed of Spark, data experts can tackle and iterate through their data issues faster.
- Apache Mahout
Apache Mahout is a library of adaptable machine learning algorithms, executed over Apache Hadoop and utilizing the MapReduce paradigm. Once big data is put away on the Hadoop Distributed File System (HDFS), Mahout gives the data science devices to consequently discover important patterns in those big data sets.
The Apache Mahout project intends to make it quicker and less demanding to transform big data into big information.
Despite the fact that it is enterprise-centered, Protege has a suite of open source tools perfect for software developers to make ‘data based applications with ontologies’. Aimed at both experts and (to some degree) beginners, Protege gives software developers a chance to make, transfer, change and share applications.
Protege likewise houses a dynamic community, making troubleshooting straightforward and joint effort optimised.
- Nervana Neon
Nervana and Intel have united to design the next generation of intelligent agents and applications and Neon is its open source Python-based machine learning library.
Established in 2014, Neon gives software developers a chance to construct, prepare and convey deep learning advancements in the cloud. Neon has heaps of video instructional exercises and a ‘model zoo’ which houses pre-prepared algorithms and illustration scripts.
OpenNN is an open source class library written in C++ which executes neural networks. This open neural systems library was in the past known as Flood. It incorporates loads of documentation and instructional exercises including a prologue to neural systems, in spite of the fact that OpenNN is aimed at software developers with lots of involvement with Artificial Intelligence.
The bundle accompanies unit testing, numerous cases and broad documentation. It gives a powerful structure to the innovative work of neural systems algorithms and applications.