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Machine learning is part of artificial intelligence, a technology that has been around for a long time, and today, it is everywhere. Machine learning tools and frameworks have made it much friendlier for the IT sector. A few decades ago, it was hard to imagine technology like machine learning being used, but today machine learning is applied in various sectors, for example, in healthcare, it is applied in medical imaging, natural language processing of medical documents, and genetic information. Machine learning (ML) is quickly transforming almost every sphere of human life. From how people communicate to transport, people seem to be getting increasingly addicted to this technology. This article features the top ten best machine learning frameworks you can utilize in 2022 and beyond.
A simple definition could be to think of it as a tool or library that allows developers to build ML models or Machine Learning applications with ease without having to get into the nuts and bolts of the base or core algorithms.
It is an open-source library developed by Google Brain that is used to deal with complex datasets and execute high-volume numerical computations. Tech giants like Nvidia, Google, Intel, and SAP are using Tensorflow. It allows developers to send counterfeit neural systems with massive datasets after setting up and training them. It is one of the best machine learning frameworks you can utilize in 2022.
PyTorch is a Facebook-developed Machine Learning framework. Its source code is available on GitHub, where it currently has over 22k stars. It has gained significant momentum since 2017 and is undergoing relentless reception growth. It is one of the best machine learning frameworks you can utilize in 2022.
Sci-Kit Learn supports development work in Python with an extensive library for Python programming language. Sci-Kit Learn is one of the best for data mining and data analysis. As it provides support for designing algorithms and models for classifications, clustering, pre-processing, regression, Dimensional reduction, and Model selection.
Spark ML can run in clusters. In other words, it can handle large matrix multiplication by taking slices of the matrix and running that calculation on different servers. (Matrix multiplication is among the crucial ML operations.) It is one of the best machine learning frameworks you can utilize in 2022.
Keras is a high-level ML API that can run on top of TensorFlow, Microsoft Cognitive Toolkit, and Theano. Its ease of use and focus on the developer experience make Keras the go-to for rapidly prototyping new apps. It is one of the best machine learning frameworks you can utilize in 2022.
CNTK can be included in projects as a library in various languages or used as a standalone machine-learning tool through its model description language called BrainScript. Skype, Bing, Cortana, and other brands are using the commercial-grade toolkit with massive datasets that require a scalable and highly optimized machine learning platform.
Shogun is an open-source Machine Learning framework that works well with C++. Again, it is free of cost and essential for developers to design algorithms and data structures, specifically for ML problems in sectors like education and research. It is one of the best machine learning frameworks you can utilize in 2022.
Auto ML is the most robust and relatively recent addition to the arsenal of tools accessible to a machine learning engineer. As you know, optimizations are critical in machine learning tasks. Thus, people reach a modern world of meta, in which software assists in creating software. AutoML is a machine learning library that many developers use to refine their models.
One of the top machine learning libraries is huggingface.co's, which creates good base models for researchers built on top of TensorFlow and PyTorch. They adapt complicated tools, such as GPT-2, to work easily on your machine. It is one of the best machine learning frameworks you can utilize in 2022.
Torch claims to be the easiest Machine Learning framework. It is an old machine learning library, first released in 2002. Before, with PyTorch, Python was the chosen method to access the fundamental tables in which Torch performs its calculations. The Torch itself can be used using Lua, with the LuaRocks Package Manager.
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