2020 has been a major tipping point for machine learning, with its increased adoption across different industries. As more and more professionals pick up machine learning skills, it is important to understand the difference between mathematical and implementation skills. Even without getting into the maths of ML, you can learn to use common machine learning frameworks to implement machine learning in your work. In this article, we’ll walk you through top machine learning frameworks that you can master.
First, What Are Machine Learning Frameworks?
Machine learning frameworks are tools or libraries that help a developer create ML models or applications without extensively using any core algorithms or any technicalities.
Each framework is designed to serve different purposes. Here are some of the most popular machine learning frameworks fit for solving your business challenges.
Tensorflow is a popular machine learning framework by Google. It is an open-source software library and has a comprehensive, flexible ecosystem of tools, libraries, and community resources. Tensorflow allows the developers to easily build and deploy ML-powered applications.
TensorFlow implements data flow graphs, where batches of data (“tensors”) can be processed by a series of algorithms described by a graph. The movements of data through the system are called “flows” – hence the name. Graphics can be assembled with C ++ or Python and can be processed on CPUs or GPUs.
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