10 Best Machine Learning Software

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Table Of Content : 1. Machine Learning Framework – Objective

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2. Best Machine Learning Software a. Apache Singa b. Shogun c. Apache Mahout d. Apache Spark MLlib e. TensorFlow f. Oryx 2 g. Accord.NET h. Amazon Machine Learning (AML) i. PredictionIO j. Eclipse Deeplearning4j

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3. Conclusion

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1. Machine Learning Framework – Objective Through this blog, we will learn Best Machine Learning Software. Also, will learn ​Machine Learning​ Frameworks: Apache Singa, Shogun, Apache Mahout, Apache Spark MLlib, TensorFlow, Oryx 2,Accord.NET, Amazon Machine Learning (AML), PredictionIO, and Eclipse Deeplearning4j in detail with their latest version.

So, let’s start Machine learning Framework Tutorial.

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2. Best Machine Learning Software Best Machine learning Softwares and Framework are discussed below.

a. Apache Singa Apache Singa – Machine Learning Software,​ p​roject was initiated by the DB System Group in 2014. They had a primary focus on distributed ​deep learning​by partitioning the model. Apache Singa provides a simple programming model. Also, works across a cluster of machines. It is primarily used in natural language processing (NLP) and image recognition. A Singa prototype accepted by Apache Incubator in March 2015. As it provides a flexible architecture for scalable distributed training. And also extendable to run over a wide range of hardware.

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Apache Singa was designed with an intuitive programming model based on layer abstraction. A wide variety of popular deep learning models are supported. Such as feed-forward models.

Also, these models are based on a flexible architecture. Although, Singa runs various synchronous, asynchronous and hybrid training frameworks.

Singa’s software stack has three main components: Core, IO, and Model. Also, the core component is concerned with memory management and tensor operations. Although, IO contains classes for reading and writing data to the disk and the network. Moreover, the model includes data structures and algorithms for machine learning models.

Its main features of Apache Singa- Machine Learning Framework: ● It includes tensor abstraction for strong support for more advanced machine learning models ● Supports device abstraction for running on varied hardware devices ● Makes use of make for compilation rather than GNU auto tool ● Improvised​ Python​ binding and contains more deep learning models like VGG and ResNet ● Includes enhanced ​IO classes​ for reading, writing, encoding and decoding files and data

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Read more about machine learning applications

b. Shogun

Shogun – Machine Learning Software, was initiated by Soeren Sonnenburg and Gunnar Raetsch in 1999. Although, is currently under rapid development by a large team of programmers.

This free and open source toolbox written in C++ provides algorithms. And also data structures for machine learning problems. Moreover, Shogun Toolbox provides the use of a toolbox. That via a unified interface from C++, ​Python​, Octave, ​R​, ​Java​, Lua and C++. Hence, it can run on Windows, Linux, and even MacOS. Shogun is designed for unified large-scale learning. That is for a broad range of feature types and learning setting. Such as like classification, regression, dimensionality reduction, clustering.

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Shogun(Machine Learning Framework) supports bindings to other machine learning libraries.

Such as LibSVM, LibLinear, SVMLight, LibOCAS, and many more.

Its features include one-time classification, multi-class classification, and test frameworks; and large-scale learning.

Read more about machine learning algorithms in detail The latest version is 4.1.0.

c. Apache Mahout

Apache Mahout – Machine Learning Software, being a free and open source project of the Apache Software Foundation. It has a goal to develop free distributed algorithms for diverse areas. Such as collaborative filtering, clustering, and classification. Mahout provides Java libraries and Java collections for various kinds of mathematical operations.

Apache Mahout is implemented on top of Apache ​Hadoop​ using the ​MapReduce​ paradigm.

Categorising content with Mahout: Mahout uses the simple Map-Reduce-enabled naïve Bayes classifier.

The latest version is 0.12.2.

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d. Apache Spark MLlib

Apache Spark MLlib is a machine learning library. It’s primary objective to make practical machine learning scalable and easy. It comprises common learning algorithms and utilities.

Also, it includes classification, regression, clustering, collaborative filtering, dimensionality reduction.

The various algorithms that have been implemented and included with MLlib are: Summary statistics, correlations, hypothesis testing, random data generation Classification and regression: ● Supports vector machines, logistic regression, linear regression, naïve Bayes classification ● Collaborative filtering techniques including Alternating Least Squares (ALS) ● Cluster analysis methods including k-means and Latent Dirichlet Allocation (LDA) ● Optimisation algorithms such as stochastic gradient descent and limited-memory BGGS The latest version is 2.0.1.

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e. TensorFlow

TensorFlow​ – Machine Learning Software, is an open source software library for machine learning. It was developed by the Google team for sorts of perceptual tasks. Also, to conduct sophisticated research on machine learning and ​deep neural networks​.

TensorFlow performs numerical computations using data flow graphs. These elaborate the mathematical computations with a directed graph of nodes and edges. Edges describe the input/output relationships between nodes. Data edges carry dynamically-sized multi-dimensional data arrays or tensors.

Read in detail about machine learning future Its features are listed below: Highly flexible: TensorFlow enables users to write their own higher-level libraries on top of it by using C++ and Python​. Also, express the ​neural network​ computation as a data flow graph.

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The latest version is 0.10.0.

f. Oryx 2

Machine Learning Software – Oryx 2, is a realization of ​Lambda architecture​. It was built on Apache Spark​ for real-time large-scale machine learning. Also, it is designed for building applications and includes packaged. That are end-to-end applications for collaborative filtering, classification, regression, and clustering.

Oryx 2 comprises the following three tiers. ● General Lambda architecture tier provides batch, speed, and serving layers. As they which are not specific to machine learning.

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Specialisation on top which, in turn. Also, to provides machine learning abstraction to hyperparameter selection, etc.

Oryx 2 consists of the following layers of Lambda architecture. Such as well as connecting elements.

Batch layer: Used for computing new results from historical data and previous results.

Speed layer: Produces and publishes incremental model updates from a stream of new data.

Serving layer: Receives models and updates, and implements asynchronous API, exposing query operations on results.

Data transport layer: Moves data between layers and takes input from external sources.

The latest version is 2.2.1.

g. Accord.NET

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Accord.NET is a .NET open source machine learning framework. As it is for scientific computing. Also, it consists of multiple libraries for diverse applications. Such as statistical data processing, pattern recognition, linear algebra, etc.

This machine learning framework is divided into libraries via the installer, compressed and NuGet packages. As it includes Accord.Math, Accord.Statistics, Accord.MachineLearning, etc.

Its features are:​ ● Matrix library for an increase in code reusability. And also a gradual change of existing algorithms over standard .NET structures. ● Consists of more than 40 different statistical distributions. Such as hidden Markov models and mixture models. ● Consists of more than 30 hypothesis tests like ANOVA, two-sample, multiple-sample, etc. ● Consists of more than 38 kernel function like KVM, KPC, and KDA. The latest version is 3.1.0.

h. Amazon Machine Learning (AML)

Amazon Machine Learning (AML) is a machine learning software for developers. It has many visualization tools and wizards. That need for creating high-end sophisticated and intelligent machine learning models. Although, without any need to learn complex ML algorithms and technologies.

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AML is based on simple, scalable, dynamic and flexible ML technology. As it used by Amazon’s ‘Internal Scientists’ community professionals. That need to create Amazon Cloud Services. AML connects to data stored in Amazon S3, Redshift or RDS. Also, can run binary classification, multi-class categorization.

The key contents used in Amazon ML are listed below: ● Data sources: Basically, it contains metadata associated with data inputs to Amazon ML. ● ML models: Also, it generates predictions using the patterns extracted from the input data. ● Evaluations: Although, it measures the quality of ML models. ● Generally, batch predictions asynchronously generate predictions for multiple input data observations. ● Real-time predictions synchronously generate predictions for individual data observations.

Key features of Amazon Machine Learning Framework are: ● ● ● ●

Basically, it supports multiple data sources within its system.

Also, it allows users to create a data source object from data residing in Amazon Redshift.

Moreover, it also allows users to create a data source object from data stored in the MySQL database.

Also, supports three types of models: binary classification, multi-class classification, and regression

i. PredictionIO

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Apache PredictionIOis an open source machine learning server. It was built on top of an open source stack for developers and data scientist. Also, it needs to create predictive engines for any machine learning task.

It consists of three core components: PredictionIO platform – its open source machine learning stack for building, evaluating. And also for deploying engines with machine learning algorithms Event Server – Basically, it is an open source machine learning analytics layer for unifying events from multiple platforms Template Gallery – Also, a place for you to download engine templates. As it is for a different type of machine learning applications

j. Eclipse Deeplearning4j Eclipse Deeplearning4j is an open-source deep-learning library for the Java Virtual Machine. It can serve as a DIY tool for​ Java​, S ​ cala​and Clojure programmers working on ​Hadoop​ and other file systems. It allows developers to configure deep neural networks. Also, we use to design in business environments on distributed GPUs and CPUs. The project was created by a San Francisco company called Skymind. Also, it offers paid support, training and an enterprise distribution of Deeplearning4j.

So, this was all about Machine Learning Framework. Hope you like our explanation.

3. Conclusion As a result, we have studied Best Machine Learning Software or Machine Learning Framework: Apache Singa, Shogun, Apache Mahout, Apache Spark MLlib, TensorFlow, Oryx 2,Accord.NET, Amazon Machine Learning (AML), PredictionIO, and Eclipse Deeplearning4j. Consequently, this Machine

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Learning Software is a booming topic nowadays. I hope this blog will help you to understand the Machine Learning Software is the best way. Furthermore, if you have any query, feel free to ask in a comment section.