Today, Machine Learning is becoming one of the new standards in this modern world of technological dimensions and it’s finding a place from simple mobile chat application to complicated software such as DNA mapping and OpenCV. Machine learning is predicted to create a million new jobs and in this blog post, we will discuss the top 4 machine learning frameworks that you might focus on your progression in machine learning principles and relevant aspects.

Why Choose Machine Learning Framework?

Machine Learning is a sub-domain of AI that enables a software or mobile app to perceive the environment and learn from the data and act as a sustainable resource to predict the results without any human interaction. The machine learning principles uses algorithms to learn from the data and produces a model to be used in the form of an application.

An effective way to describe a machine learning framework is ready to use a set of libraries or a set of tools that provides accessibility to effectively develop machine learning models without delving into severe complexities of the machine learning algorithms. A mobile learning framework provides great benefits, some of them are as follows:

1. Machine Learning Frameworks are built to hide the internal complexities and makes it quite easy for a user to focus on the application goal, instead of the internal working operations of the algorithm.
2. ML Frameworks are being regularly optimized and updates along with new features are critical fixes are being performed regularly.

Let’s dive into the discussion to know about the machine learning frameworks out there in the marketplace.

Machine Learning Framework 1: PyTorch

PyTorch is an open-source python distribution or package built by Facebook and it is widely used in various areas of computing including Artificial Intelligence and Natural Language Processing (NLP). PyTorch has been developed for deep learning research having maximum flexibility and performance metrics, it is also a replacement of NumPy ndarray with Tensors that makes use of Graphical Processing Units (GPU) computation regardless of CPUs makes extreme performance-oriented metrics. Some of the pros of PyTorch are as follows:

• Easy to Learn and Use
• Well Documented
• Supports Dynamic Neural Networks
• Faster Responsiveness as it built to harness the power of GPUs and can be used to build and execute a large number of neural networks

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 Machine Learning Framework 2: Apache MXNet

Apache MXNet is the most preferred and a great open-source deep learning framework specifically designed to be efficient, flexible and productive. MXNet is portable, lightweight, and highly scaled to use diversified CPUs on multiple machines. This machine learning framework is being used by leading corporate companies including Amazon for Deep Learning Web Services. The advantage of Apache MXNet Framework are as follows:

• Apache MXNet provides support for multiple programming languages including Python, C++, Julia, Perl, Scala, Java, and R.
• This machine learning framework is very well documented not only for Python but it is supported by various programming languages including C#, Java & R.
• Toolkits and packages are powered with MXNet and are available to expand their functionalities and methodological instances.

 Machine Learning Framework 3: TensorFlow

TensorFlow is one of the most powerful, reliable and open-source machine learning framework that is created and preserved by Google. TensorFlow can be used on a wide range of products built by Google such as Gmail, Google Photos, Speech Recognition, and NLP. The machine learning framework can be performed by complex research on Machine Learning and Deep Learning Principles.  Some of the advantages of Tensor Flow are described below:

• TensorFlow is an open-source, free and reliable machine learning framework across the global searches on Google.
• TensorFlow allows deep learning and it is very easy to implement.
• TensorFlow is recognized by many employees as a technical skillset.

Tensorflow supports multiple programming languages like C++, Java, C#, Python, MATLAB, Ruby and more.

• TensorFlow has an extensive set of technical documentation for developers, tester and many more.

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Machine Learning Framework 4: Scikit-Learn

Scikit-Learn is another very well-known machine learning framework that is widely used in the developers’ community. It is an open-source and free to use machine learning framework. The library is built upon SciPy (Scientific Python) library and the framework includes NumPy, Matplotlib, IPython, Symp and Pandas libraries. The framework provides a wide range of supervised unsupervised learning algorithms. Seemingly, there are several advantages of Scikit-Learn framework are as follows:

• It is a well-known, open-source and free to use in commercial instances.
• The framework is very easy to use and learn.
• It provides extensive documentation for developers.
• It is sponsored by the international community as the framework update, bug fixes, and new features are released regularly.

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