Technology & its development is on a speed train going faster than ever in present times. We’ve witnessed the origin of the internet, smartphones, next-gen gaming consoles and much more. However, there is one particular technological advance that has managed to somehow overshadow all that we’ve seen so far. You’ve probably guessed it by now. Yes, it’s Artificial Intelligence. Artificial Intelligence, often abbreviated as AI, is basically a simulation of human intelligence. It’s developed and deployed to assist us in various tasks by automating and executing. Ever wondered how it does all of that? Machine Learning is an AI branch dealing with the use of algorithms & data to imitate our ‘learning’ process. Machine Learning exists to improve AI’s accuracy as per our standards. In this article, we’ll delve deeper into Machine Learning, Machine Learning Tools and Libraries. Machine Learning carries great importance & today we’ll learn about tools & libraries developed around it.
Machine Learning is an essential branch of Artificial Intelligence. There are plenty of AI tools out there on the internet. These tools are developed to serve various purposes with a common objective of helping us by saving precious time. Similarly, there are some tools specializing in Machine Learning as well. Do they work like AI tools do? Not exactly. AI tools built around machine learning don’t always provide straight, to the point solutions. When dealing with the niche of Machine Learning, most of the time people don’t even search for Machine Learning Tools. However, there is something called a Machine Learning Library. It can be said that a Machine Learning Library is the tool for Machine learning.
In this article, we’ll be hopping on a systematic approach to enhance our understanding of Machine Learning Libraries. We’ll follow that up with an insightful comparison of three widely popular Machine Learning Libraries.
What are Machine Learning & ML Tools?
Before diving into a comparison of ML Libraries. It is important to understand the concept of Machine Learning. Just like Computer Vision & Natural Language Processing (NLP), Machine Learning is an important branch of AI. It is of utmost significance and has a wide range of applications. Machine Learning deals with using elements like data & algorithms to copy our learning process. There are a few fundamental objectives it aims at achieving. The most primary one of them is to imitate our learning process and use it to increase Artificial Intelligence’s accuracy. Machine Learning views our learning process as the key to enhance AI’s accuracy, improving all of it in general.
As we mentioned before, there aren’t any simple & direct applications of Machine Learning in the form of ML tools. Instead, we have Machine Libraries. These libraries are a collection of commonly used utilities & algorithms. These utilities & algorithms have their own individual uses and applications. AI tools designed for a variety of purposes make use of these algorithms to execute tasks. A Machine Learning Library is where one will find the most commonly used utilities and algorithms stored and organized neatly.
Now that we’ve learnt about Machine Learning and what Machine Learning Libraries are, let’s move forward with our comparison. We’ll be taking three globally popular Machine Learning Libraries into consideration. So without any further delay, let’s begin.
Machine Learning Tools
TensorFlow is one of the most popular Machine Learning Libraries out there across the internet. It is home to a large number of highly valuable algorithms and utilities. TensorFlow features Machine Learning services and aids for every existing skill level. It addresses problems for every stage of your workflow and is ready with a solution for it. Developers across the globe rely on it to develop and train models for various purposes. TensorFlow provides assistance by featuring some pre-trained models for reference or for free use. It also is a suitable platform for developers to build ML based models of their own. Its open source ML library is what defines its functionality as an exceptional one. TensorFlow is an ideal choice for developers of all skill levels to use, develop and train AI models. It also features a section where a complete beginner gets to learn machine learning.
Overall, TensorFlow is a complete Machine Learning solution with a rich library to help you get going. It is one of the best ML Libraries out there.
PyTorch is yet another widely famous Machine Learning Library. It is a highly capable platform featuring some of the best utilities and algorithms in the field of Machine Learning. PyTorch’s key features and capabilities rank it highly in the list of the best Machine Learning Libraries. It boasts a brilliant ecosystem dealing with AI and its branches like NLP, Computer Vision and much more. It helps developers reach production from research at an impressive speed. PyTorch also provides you with a massive community, giving you an opportunity to interact and grow with other developers.
AI’s functions are categorized on the basis of the purpose they serve. These include classification, regression, clustering, dimensionality reduction, model selection, preprocessing etc. It is these categories that spread out further into separate AI branches like Computer Vision, NLP and much more. Scikit Learn boasts a user-friendly interface. It displays its collection of algorithms in a well curated manner under their respective categories. As a Machine Learning Library, it is a great platform to find the algorithms you’re looking for in quick seconds. It is one of the top Machine Learning Libraries out there.
Machine Learning is an advanced branch of AI responsible for training AI models to grow more accurate and precise. It acts as an element of origin to several other AI applications. Machine Learning Libraries feature the algorithms & utilities required to develop and train an AI model. They also provide several other features related to AI development and training. In this article, we drove deeper into the concepts of Machine Learning. Moreover, we also learnt about some of the best Machine Learning Libraries out there and compared them. We believe that TensorFlow, PyTorch & Scikit Learn, all of them are greatly viable as MLlibs and bring great value. They also provide features and perks exclusive to themselves. It is up to a developer to choose which one of them is an apt choice for them. We’ll now wrap this article up with hopes that it was of help to you.