Welcome to the future, where concepts like artificial intelligence and machine learning have left the realm of science fiction books and movies and entered our reality! But thereâ€™s no need to joyously welcome our robot overlords just yet. It turns out they still need us to handle the programming.
For now, anyway!
Artificial Intelligence (AI) and machine learning (ML) are gaining increasing traction in todayâ€™s digital world. This popularity is why IT professionals should master concepts such as machine learning tools and machine learning algorithms.
Today, we are exploring machine learning libraries, specifically Python machine learning libraries. We offer a preview of the top libraries ML professionals will be using in 2021 so that people can get a jump on the new year.
We begin with a few definitions to make sure everyoneâ€™s up to speed.
Although itâ€™s tempting to conflate AI and ML, they are two distinct concepts.
In summary, AI is a catch-all term for teaching machines how to think and accomplish tasks like humans, and ML is a type of AI where computers receive data and learn on their own.
In the deep, dark, ancient days of early machine learning, programmers conducted ML tasks by coding the statistical and mathematical formulae and every algorithm by hand. This approach was time-consuming, inefficient, and tedious.
Today, libraries, modules, and frameworks handle those monotonous tasks. Libraries contain modules and codes that provide system functionality and standardized solutions for most everyday programming problems and issues.
Libraries make it easy for organizations to benefit from the countless machine learning applications without wasting time and resources.
This simplicity makes sense when you consider the best way to teach a person a new language or subject is by using basic, easy to understand words and phrases. Clearly, machines are no different.
Hereâ€™s a summary of why you might want to learn about Python machine learning libraries.
If youâ€™re currently in a machine learning-related career or anticipate starting one soon, here are the twelve most common machine learning libraries you will most likely work with.
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PyTorch has two predominant, high-level features:
PyTorch has a vast selection of tools and libraries that support computer vision, natural language processing (NLP), and a host of other Machine Learning programs. Pytorch allows developers to conduct computations on Tensors with GPU acceleration and aids in creating computational graphs. Considered one of the best deep learning and machine learning frameworks, it faces stiff competition from TensorFlow.
Scikit-learn supports algorithms like:
Tensorflow defines and runs computations involving tensors, hence the clever name. It trains and runs deep neural networks, which in turn develop many AI applications. TensorFlow is often used in the deep learning research and application fields.
Hay, I am the BOSS. You Know!
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