Machine learning tools for developers

Author:- Careers of Tomorrow

Artificial intelligence is gaining popularity and with it comes a growing number of machine learning tools and software developers can use. There is quite a number of software; knowing which one to use will make a difference. A developer can be at an edge by knowing different AI frameworks and APIs to enable learning of new skills as technology grows.

  1. Accord.NET

Accord.NET is a machine learning framework for scientific computing with a combination of audio and imagery processing libraries. Especially for designers and developers, this framework helps in building applications for commercial use. Applications such as pattern recognition, machine learning can be built.

It is allocated across multiple libraries. Users can choose across these libraries. These comprise image processing, signal processing, scientific computing, and support libraries. It comes with features like face detection, natural learning algorithms and more.

  1. H2O

One of the top machine learning tools is H2O that works well for businesses and developers. It is an open source software tool that was designed by that is set in with a machine learning platform. The tool is written in Java, Python and R.

Written in the languages that developers are acquainted with, this platform enables for ease in applying machine learning and predictive analysis. It can be downloaded on Linux, MacOS and Windows OS. The platform is used to evaluate datasets in cloud and Apache Hadoop.

  1. Apache PredictionIO

Apache PredictionIO supports machine learning and data processing libraries. It is an open source machine learning server which helps in making data infrastructure management simpler. The server helps build predictive engines for machine learning tasks.

It comprises three fundamental components:

  • PredictionIO platform – Through machine learning algorithms, the platform enables developers to evaluate and deploy engines. Developers and data scientists can employ the platform without limits.
  • Event Server – The server constantly gathers data from your application and unifies data from multiple platforms.
  • Template Gallery – Engine templates are available for download for various types of machine learning applications.
  1. Eclipse Deeplearning4j

Eclipse Deeplearning4j provides components to build AI applications. It is an open-source deep-learning library which enables developers to configure deep neural networks. It helps provide smart defaults to build deep learning applications. This deep-learning library aims to help programmers in Java and Scala. It is designed for usage in business environments by a San Francisco company, Skymind.

  1. Torch

Torch is an open source machine learning library that supports wide ML algorithms. It is a scientific computing framework which is easy to use as its script is based on LuaJIT. It is in use within Facebook, Google, Twitter, and other companies. It provides an array of algorithms for deep machine learning. Torch gives the flexibility to build scientific algorithms. It comes with community-driven packages in the processing of image, video, audio, machine learning, computer vision

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