AI News, 5 Open Source Libraries to Aid in Your Machine Learning Endeavors

5 Open Source Libraries to Aid in Your Machine Learning Endeavors

Machine learning is changing the way we do things, and it’s becoming mainstream very quickly.

Related:5 Strategies From Top Firms on How to Use Machine Learning While many factors have contributed to this increase in machine learning, one reason is that it’s becoming easier for developers to apply it, thanks to open source frameworks.

It’s a file with re-usable code that can be shared by many applications, so you don’t have to write the same code repeatedly.

If you’re diving into machine learning in a big way, you’re probably seeking resources to help guide you.

Amazon Machine Learning.Amazon Machine Learning (AML) is built for developers, with many tools and wizards to help you create machine learning models without having to learn all the complexities of how machine learning works.

Accord.NET.Accord.NET,a NET machine learning framework, has multiple libraries to handle everything from pattern recognition, image and signal processing tolinear algebra, statistical data processing and more.

Accord is useful because it has so much to offer, including 40 different statistical distributions, more than 30 hypothesis tests, and more than 38 kernel functions.

It brings together many learning algorithms and utilities, including classification, clustering, dimensionality reductionand more.

Welcome to Apache™ Hadoop®!

This is a maintenance release of Apache Hadoop 2.7.

It contains 249 bug fixes, improvements and other enhancements since 3.0.2.

Users are encouraged to read the overview of major changes since 3.0.2.

For details of 249 bug fixes, improvements, and other enhancements since the previous 3.0.2 release, please check

It contains 77 bug fixes, improvements and enhancements since 2.8.3.

Users are encouraged to read the overview of major changes for major features and improvements for Apache Hadoop 2.8.

For details of 77 fixes, improvements, and other enhancements since the 2.8.3 release, please check

It contains 208 bug fixes, improvements and enhancements since 2.9.0.

Users are encouraged to read the overview of major changes for major features and improvements for Apache Hadoop 2.9.

For details of 208 fixes, improvements, and other enhancements since the 2.9.0 release, please check

This release fixes the shard jars published in Hadoop 3.0.1.

Hadoop 2.7.6 Release Notes for the full list of 46 bug fixes and

particularly enables POSIX groups support for LDAP groups mapping service.

It contains 768 bug fixes, improvements and enhancements since 3.0.0

Users are encouraged to read the overview of major changes since 3.0.0.

For details of 768 bug fixes, improvements, and other enhancements since the previous 3.0.0 release, please check release notes and changelog detail the changes since 3.0.0.

It contains 49 bug fixes, improvements and enhancements since 3.0.0.

Please note: 3.0.0 is deprecated after 3.0.1 because HDFS-12990 changes NameNode default RPC port back to 8020.

Users are encouraged to read the overview of major changes since 3.0.0.

For details of 49 bug fixes, improvements, and other enhancements since the previous 3.0.0 release, please check release notes and changelog detail the changes since 3.0.0.

Hadoop 2.7.5 Release Notes for the list of 34 bug fixes and

After four alpha releases and one beta release, 3.0.0 is generally available.

3.0.0 consists of 302 bug fixes, improvements, and other enhancements since 3.0.0-beta1.

All together, 6242 issues were fixed as part of the 3.0.0 release series since 2.7.0.

Users are encouraged to read the overview of major changes in 3.0.0.

The GA release notes and changelog detail the changes since 3.0.0-beta1.

It contains 79 bug fixes, improvements and other enhancements since 2.8.2.

For major features and improvements for Apache Hadoop 2.8, please refer:

For details of 79 fixes, improvements, and other enhancements since the previous 2.8.2 release, please check:

It includes 30 New Features with 500+ subtasks, 407 Improvements, 790 Bug fixes new fixed issues since 2.8.2.

For details of 790 bug fixes, improvements, and other enhancements since the previous 2.8.2 release, please check:

Please note: Although this release has been tested on fairly large clusters, production users can wait for a subsequent point release which will contain fixes from further stabilization and downstream adoption.

It contains 315 bug fixes, improvements and other enhancements since 2.8.1.

For details of 315 fixes, improvements, and other enhancements since the previous 2.8.1 release, please check:

It consists of 576 bug fixes, improvements, and other enhancements since 3.0.0-alpha4.

Please note that beta releases are API stable but come with no guarantees of quality, and are not intended for production use.

Users are encouraged to read the overview of major changes coming in 3.0.0.

The beta1 release notes and changelog detail the changes since 3.0.0-alpha4.

It consists of 814 bug fixes, improvements, and other enhancements since 3.0.0-alpha3.

Please note that alpha releases come with no guarantees of quality or API stability, and are not intended for production use.

Users are encouraged to read the overview of major changes coming in 3.0.0.

The alpha4 release notes and changelog detail the changes since 3.0.0-alpha3.

Please note that 2.8.x release line continues to be not yet ready for production use.

It consists of alpha2 plus security fixes, along with necessary build-related fixes.

Please note that alpha releases come with no guarantees of quality or API stability, and are not intended for production use.

Users are encouraged to read the overview of major changes coming in 3.0.0.

The alpha3 release notes and changelog detail the changes since 3.0.0-alpha2.

For details of 2917 fixes, improvements, and new features since the previous 2.7.0 release, please check:

This is the second alpha in a series of planned alphas and betas leading up to a 3.0.0 GA release.

The intention is to 'release early, release often' to quickly iterate on feedback collected from downstream users.

Please note that alpha releases come with no guarantees of quality or API stability, and are not intended for

Users are encouraged to read the overview of major changes coming in 3.0.0.

The alpha2 release notes and changelog detail 857 fixes, improvements, and new features since the previous 3.0.0-alpha1 release.

This is the first alpha in a series of planned alphas and betas leading up to a 3.0.0 GA release.

The intention is to 'release early, release often' to quickly iterate on feedback collected from downstream users.

Please note that alpha releases come with no guarantees of quality or API stability, and are not intended for

Users are encouraged to read the overview of major changes coming in 3.0.0.

The full set of release notes and changelog detail all the changes since the previous minor release 2.7.0.

2.7.0 section below for the list of enhancements enabled by this

One of Yahoo's Hadoop clusters sorted 1 terabyte of data in 209 seconds, which beat the previous record of 297 seconds in the annual general purpose

Top 10 machine learning frameworks

When delving into the world of machine learning (ML), choosing one framework from many alternatives can be an intimidating task.

There are different frameworks, libraries, applications, toolkits, and datasets in the machine learning world that can be very confusing, especially if you’re a beginner.

This open source framework is being used for extensive research on deep neural networks and machine learning.

Caffe Caffe is a machine learning framework that was designed with better expression, speed, and modularity as the focus points.

If you are dealing with applications with text, sound or time series data, note that Caffe is not intended for anything other than computer-vision.

It is a collection of tools and wizards that can be used for developing sophisticated, high-end, and intelligent learning models without actually tinkering with the code.

The technology behind AML is used by Amazon’s internal data scientists to power their Amazon Cloud Services and is highly scalable, dynamic and flexible.

AML can connect to the data stored in Amazon S3, RDS or Redshift and carry out operations such as binary classification, regression or multi-class categorization to create new models.

Apache Singa Apache Singa is primarily focused on distributed deep learning using model partitioning and parallelizing the training process.

Singa was developed with an intuitive layer abstraction based programming model and supports an array of deep learning models.

Having support for a wide variety of machine learning algorithms such AS CNN, LSTM, RNN, Sequence-to-Sequence and Feed Forward, it is one of the most dynamic machine learning frameworks out there.

Torch Torch could arguably be the simplest machine learning framework to set up and get going fast and easily, especially if you are using Ubuntu.

Some of the perks of Torch can be attributed to this friendly programming language with useful error messages, a huge repository of sample code, guides, and a helpful community.

It consists of different libraries that can be used for applications like pattern recognition, artificial neural networks, statistical data processing, linear algebra, image processing etc.

Apache Mahout Being a free and open source project by the Apache Software Foundation, Apache Mahout was built with the goal of developing free distributed or scalable ML frameworks for applications like clustering, classification, and collaborative filtering.

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