TensorFlow is one of the most popular deep learning frameworks out there. And what makes it particularly appealing is its ecosystem. So you can develop and train models using Python amongst several other languages, and then easily deploy them in the cloud or on-prem, in the browser, or on mobile devices.
TensorFlow was released in 2015 by Google Brain Team. TensorFlow is Google’s brainchild for machine learning and high-performance numerical computation. Simply put, TensorFlow is an open-source AI framework to perform complex numerical computations in large volumes using data-flow graphs and machine learning. TensorFlow operates on deep neural networks.
We all have been using TensorFlow without even realizing its presence: from Google Photos to Google voice, these applications operate on large clusters of Google hardware.
You can use Tensorflow for all sorts of machine learning tasks, such as image classification, all the way to natural language processing.
At its core, it’s very similar to NumPy, but with GPU support. So it supports distributed computing, which means it works across multiple devices and services and it also includes a just-in-time compiler that allows it to optimize computations.
In my Data Science course, I detail real-life examples of how these AI frameworks are deployed and interpreted. I simplify important AI concepts for Data Science enthusiasts.
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