Machine Learning is slowly becoming one of the new standards in the technological world and it is finding its place from simple mobile chat applications to complex software like DNA mapping. Machine Learning is predicted to create a million new jobs and in this blog post, we will talk about the Top 5 Machine Learning Frameworks that you might want to consider if you are looking for a career in machine learning.
What is Machine Learning Framework
Machine learning is a sub-field of Artificial Intelligence that allows a software application to learn from the data and become more accurate in predicting the outcome without the need for any human interaction. Machine learning uses algorithms to learn from the data and generates a model to be used in an application. Knowing about the algorithms is very important to achieve the desired output but creating them is not required as many companies and open source communities provide a framework that provides all the tools and functionalities to achieve the desired outcome in a quick and efficient way.
A quick way to define a machine learning framework is, it is ready to use a set of libraries or a set of tools that allows a user to quickly build machine learning models without worrying about the inner complexities of underlying algorithms. Using a machine learning framework provides great advantages, few of them follows:
- Frameworks are built to hide the internal complexities and make it very easy for a user to focus on the application goal, rather than the internal working of the algorithms.
- Frameworks are regularly updated; new features and critical fixes are done at regular intervals.
- Frameworks are optimized and build for high performance.
- Frameworks come with features that help to define the required pipeline to building a model and deploying them.
Machine Learning Frameworks to consider in 2020.
TensorFlow is one of the most well-known, open-source machine learning framework created and maintained by Google. TensorFlow can be used on a broad range of Google products like Gmail, Google Photos, Speech Recognition. The framework can perform complicated research on Machine learning and Deep learning. A few of the advantages of TensorFlow are as follows:
- TensorFlow is open-source, free and reliable as Google is behind it.
- TensorFlow allows deep learning and it very easy to implement.
- TensorFlow is recognized by many employers as a skillset.
- It has extensive documentation for developers.
Scikit-Learn is another very well-known machine learning framework among the Python developers community. It is also open-source and free to use framework. The library is built upon SciPy (Scientific Python) library and the framework includes NumPy, Matplotlib, IPython, Sympy, and Pandas libraries. The framework provides a range of supervised unsupervised learning algorithms. A few of the advantages of the Scikit-Learn framework are as follows:
- It is open-source and free to use even commercially.
- The framework is very easy to use and learn.
- It has extensive documentation for developers.
- It is backed by the international community hence the framework update, bug fixes, and new features are released regularly.
PyTorch: is an open-source python package developed by Facebook and is mostly used in Natural Language Processing (NLP). PyTorch has been built for deep learning research having maximum flexibility and speed and is also a replacement of NumPy ndarrays with Tensors that make use of GPUs computation instead of CPUs making it extremely performant. A few of the advantages of PyTorch are as follows:
- Very easy to learn and use, as it is built to be deeply integrated into Python, so if you already know Python you are already at home to use PyTorch.
- PyTorch is very fast as it built to harness the power of GPUs and can be used to build and run large neural networks.
- PyTorch is well documented.
- It supports dynamic neural networks.
Apache MXNet: Apache MXNet is another great open-source deep learning framework that is designed to be efficient, flexible, and enhance productivity. MXNet is portable, lightweight, and can easily scale to use multiple CPUs on multiple machines. The framework is being used by Amazon is Deep Learning Web Services. A few of the advantages of using the Apache MXNet framework are as follows:
- Apache MXNet has support for multiple major programming languages like Python, C++, R, Julia, Perl, Scala, Closure, and Java.
- Apache MXNet has a very small memory footprint hence the model can be trained and build on cloud and can be easily deployed on mobile phones or connected devices.
- The framework is very well documented not only in Python but all supported programming languages.
- Toolkits and packages built around MXNet are available to extend the functionalities.
Microsoft Azure ML: Microsoft Azure Machine Learning is a cloud-based predictive-analytics service. It comes with a browser-based tool that provides a very easy, drag and drop interface called Azure Machine Learning Studio (ML Studio) for building machine learning models. The generated model can be easily deployed as a Web Service that can be consumed by any programming language of your choice. Few advantages of using the framework are:
- A beginner can also use Azure Machine Learning Studio (ML Studio) to build models with little or no experience.
- The framework bundles a set of algorithms that a real-world tested by Microsoft.
- The generated model can be exposed as a Web Service and can be used on any device of choice which is programming language independent.
- The machine learning model can be easily customized using R or Python which have built-in support within the framework.
Here are the top 5 machine learning frameworks that I as a beginner would look into. If you are a beginner and hoping to train in machine learning I would request you to learn Python if already not familiar with it as most of the frameworks have support for it and to start with Azure ML Studio just to clear the model generation workflow with easy drag and drop. I hope you found this post helpful, Cheers !!!.
[Further Readings: Visual Studio 2019 Output Window | Visual Studio 2019 Code Navigation (Ctrl+T) | 10 Basic Machine Learning Terminologies | Introduction to Machine Learning | How to Publish a NET Core application | How to change Visual Studio 2019 Theme | How to create an ASP NET Core MVC Web Application using dotnet-cli | How to create an ASP.NET Core Web Application using dotnet-cli | How to create a dotnet core NUnit Test Project using dotnet-cli | How to create a dotnet core xUnit Test Project using dotnet-cli | How to create a dotnet core MSTest Project using dotnet-cli | How to create dotnet core WinForms Application using dotnet-cli ]