10 Machine Learning Frameworks That You Need To Know

When delving into the globe of machine learning, selecting one framework from several alternatives may be a difficult task. There are totally different frameworks, libraries, applications, toolkits, and datasets within the machine learning world which will be terribly confusing, particularly if you are a beginner.

Being aware of the different Machine Learning frameworks is important as it involves selecting one to create your application.

1. Tensorflow:

This open source framework is used for in-depth analysis of deep neural networks and machine learning. Being the second machine learning framework by Google Brain, it is compatible with most new CPUs and GPUs. Several research papers from Kolkata on identification of handwritten Bangla numerals employs Tensorflow to implement Machine Learning and CNN. Tensorflow uses information flow graphs to perform sophisticated numerical tasks.

The mathematical computations are detailed, employing a directed graph containing edges and nodes. These nodes are used to implement the operations and might conjointly act as the endpoints where information is fed.
Tensorflow
Courtesy: Tensorflow

2. Caffe:

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

Caffe is well-liked for its Model zoo, a set of pre-trained models that do not need any coding to implement. If you are handling applications with text, sound or statistical information, note that Caffe is not meant for the world but computer-vision only.
 caffe
Courtesy: Caffe

3. Amazon Machine Learning:

Amazon has developed their own machine learning service for developers referred to as AML. It is a set of tools and wizards which will be used for developing refined, high-end, and intelligent learning models while not truly tinkering with the code.

Using AML, predictions required for your applications may be derived via APIs that are easier to use.

The technology behind AML is employed by Amazon's internal data scientists to power their Amazon Cloud Services and is very ascendable, dynamic and versatile. AML will connect with the information kept in Amazon S3, RDS or Redshift and perform operations equivalent to binary classification, regression or multi-class categorization to form new models.
amazon
Courtesy: Amazon

4. Apache Singa:

Apache Singa is primarily centred on distributed deep learning, victimization model partitioning and parallelizing the coaching method. It provides a straightforward and sturdy programming model which will work across a cluster of nodes. The main applications are in image recognition and natural language processing.

Singa was developed with associate intuitive layer abstraction, based mostly on a programming model and supports an array of deep learning models. The technical school stack of Singa contains 3 vital components: IO, Model and Core.

The IO element contains categories used for reading/writing information to the network and disk. Model stores algorithms and data structures used for machine learning models.
Apache
Courtesy: Apache

5. Microsoft CNTK:

CNTK is Microsoft's open-source machine-learning framework. Having support for a large kind of machine learning algorithms equivalent to CNN, LSTM, RNN, Sequence-to-Sequence and Feed Forward, it is the foremost dynamic machine learning framework out there.

Compatibility is one of the highlights of CNTK. It is conjointly praised as the most communicative and simple to use machine learning design yet. On CNTK, you will work with languages like C++ and Python and either use the inbuilt training models, or build your own.
microsoft
Courtesy: Microsoft

6. Torch:

Torch might arguably be the only machine learning framework to line up and get going; quick and simple, particularly if you are using Ubuntu. Developed in 2002 at NYU, Torch is extensively employed in massive technical school firms like Twitter and Facebook.
courtesy: torch
Courtesy: Torch

7. Accord.Net:

Accord.NET is an open source machine learning framework based on .NET framework. It consists of various libraries used for applications like pattern recognition, artificial neural networks, statistical data processing, algebra, image processing etc. The framework contains libraries that are accessible as installers, NuGet packages and source code.
Accord.Net
Courtesy: .Net

8. Apache Mahout

Being a free and open source project by the Apache software package Foundation, Apache mahout was designed with the goal of developing free distributed or ascendable ml frameworks for applications like clustering, classification and collaborative filtering.

Apache Mahout is deployed on top of Hadoop using the MapReduce paradigm. One nice application is to instantly flip information into insights. Once the hold on massive knowledge on Hadoop is connected, the driver will facilitate the data science tools to find significant patterns from the datasets.
Apache Mahout
Courtesy: Apache

9. Theano:

Theano was developed in 2007 at the University of Montreal that is world renowned for machine learning algorithms. Although considered a low-end machine learning framework, it is versatile and blazing quick. The error messages thrown by the framework are ill-famed for being unhelpful and cryptic.
Theano
Courtesy: Theano

10. Brainstorm:

Brainstorm is one of the simplest machine learning frameworks to master considering its simplicity and adaptability. Brainstorm provides two 'handers' or data APIs using Python - one for CPUs by Numpy library and also the alternative one, to leverage GPUs using CUDA. Most of the work is finished by Python scripting which implies a fashionable front-end UI is nearly absent.

A hammer is of no use to drive a screw. You will always need to bring the right tools for the job. Identifying the task at hand and understanding the data obtainable from them are essential in selecting the correct machine learning framework you would need for any particular task.
Brainstorm
Courtesy: Brainstorm

Conclusion:

For a fresher, it is important to choose the right kind of tools for the right kind of job. Choosing the correct framework is the foundation of any machine learning project.

Resources:

http://bigdata-madesimple.com/top-10-machine-learning-frameworks
https://dzone.com/articles/ten-machine-learning-algorithms-you-should-know-to

 

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