8 leading open-source frameworks for AI and ML model

NovelVista

Last updated 22/07/2021

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AI (Artificial Intelligence) and ML (Machine Learning) have literally taken over this decade. Don’t you think? 

From the day of the Dartmouth Conference in 1956, where the concept of AI took birth, it has come a very long way. Today, 37% of organizations across the world have implemented AI in some form or the other. A Gartner report says, there is a 270% rise in the field of AI over the past four years. Can you imagine that?

Machine learning as well is making its way to the organizations faster than a Bugatti Chiron. In 1959, Arthur Samuel, an American pioneer in the field of Artificial Intelligence and computer gaming first coined the term ‘Machine Learning’. Machine Learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of Artificial intelligence. And now, 1 out of 3 IT leaders are planning to use ML for business analytics. Can you imagine how far it has come?

But, if you want to make the best out of AI and Machine Learning, it is quite obvious that you’ll need the latest tools and frameworks to make it work. The question is, where to find them?

In this blog, we are going to tell you about the open-source frameworks that you can use to make the best out of AI and ML models. Go through thoroughly, and pick up what’s best for you!

Open-source software (OSS) is a type of computer software in which source code is released under a license in which the copyright holder grants users the rights to study, change, and distribute the software to anyone and for any purpose. Open-source software may be developed in a collaborative public manner. Open-source software is a prominent example of open collaboration. And guess what? We have listed out 8 of them for you! Let’s check out what they are.

Top 8 Open Source AI & Machine Learning Model

1. Torch:

While Torch’s makers call it the easiest ML framework, its complexity is relatively simple that comes from its scripting languages interface. The programming language is full of numbers that are not categorized by numbers like any other language. 

There are some big giants of corporates who are using Torch nowadays like Facebook AI research group, IBM, Yandex, Idiap Research Institute, etc.

Advantages of using Torch:

• Flexible to use
• Provides a high level of speed and frequency
• Lots of pre-trained models available
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• 2. Caffe:

• Caffe or Convolutional Architecture for Fast Feature Embedding, a deep learning tool developed by UC Berkeley, is mainly written in CPP. Hence, it supports many different types of architecture on image classification and segmentation. 
• Caffe has been mainly used by startups and academic project researchers. Recently, Yahoo has integrated Caffe as well!

Advantages of using Caffe:

• The fastest way to apply deep neural networks to the problem
• Well organized Mat lab and python integration
• Supports out of the box GPU training

3. Shogun:

It is created with C++ but it can be used with other languages as well like Java, Python, Ruby, C#, etc. This tool is curated for large scale learning. Mainly, it focuses on Kernel machines like support vector machines and classification and regression problems.

Advantages of using Shogun:

• Allows linking to other AI and Machine learning libraries
• Processes a large amount of data
• Wide range of standard and cutting edge algorithm
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4. Tensorflow:

This one is the most popular tool for every ML specialist. This tool implements data flow graphs where data can be processed by a series of algorithms described by the graph. 

Tensorflow contains a large amount of documentation, training materials, and online resources. Besides, Google has long term plans for Tensorflow through third-party developers. 

Advantages of using Tensorflow:

• An end-to-end deep learning system
• Highly flexible
• Performs numerical computation with the help of the data flow graph
• Runs on CPU, GPU, and mobile computing platforms
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5. Theano:

Theano is the Python library that allows you to define, optimize and evaluate mathematical expressions involving multi-dimensional array efficiently. You can easily integrate this tool with NumPy, dynamic C code generation, and symbolic differentiation. 

Advantages of using Theano:

• Provides high speed
• Can compete toe-to-toe with the speed of hand-crafted C language implementations

6. Azure Machine Learning Studio:

Microsoft Azure Machine Learning Studio is a collaborative tool that can be used to build, test and deploy predictive analytics solutions on your data. This tool publishes models that can be used as web services. 

Advantages of using Azure ML Studio:

• Provides an interactive workspace
• Connectivity with drag and down data sets

7. Net:

Accord is a .Net machine learning framework developed to build production-grade computer vision, computer audition, signal processing and statistics application. This well-documented framework makes audio and image processing simpler. Accord.Net can be used for numerical optimization, artificial neural networks, and visualization.

Advantages of using Accord.Net:

• Supports Windows
• Consists of more than 40 parametric and non-parametric estimation of statistical distributions

8. Apache Spark:

Another scalable machine learning library that runs on Hadoop. Several algorithms are included here like naive Bayes, generalized linear regression, K-means and many more. 

Advantages of Apache Spark:

• It is easily usable in Java, Scala, Python and R
• Hadoop data source like HDFS, HBase, or local files can be used
• Contains high-quality algorithms and outperforms better than MapReduce

Overview:

So now you know how you can implement AI and Machine Learning modules with the open-source networks. If you think we have missed out on any popular Open Source network that our viewers must know about, do let us know in the comment section.

We will come soon with some trending topics that are being cooked for you. Till then, stay upskilled!

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