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The TensorFlow AI framework

Machine Schooling

Article from ADMIN 57/2020
By
The TensorFlow symbolic math library can help you introduce artificial intelligence, deep learning, and neural networks into your projects.

Those working in IT and in the AI environment often know little more about TensorFlow than that it has something to do with artificial intelligence (AI). In fact, TensorFlow is one of the most powerful artificial intelligence frameworks. In contrast to some abstract projects from university research labs, it can be used today. In this article, I introduce the topic and look into the features that TensorFlow offers and contexts in which it can be used sensibly.

Basics

Wikipedia describes TensorFlow as "a free and open-source software library for dataflow and differentiable programming across a range of tasks" [1]. If you don't happen to be a math genius or an IT scientist, this description might make it difficult to understand what TensorFlow is actually about.

To begin, it makes more sense to look at TensorFlow from a different perspective: What does the program do for developers, and which problems can it help solve? To understand this, you must understand some more technical terms: What does machine learning actually mean in a technical sense? What is deep learning, and how does a neural network work? All these concepts appear regularly in the TensorFlow context, and without understanding them, TensorFlow cannot be understood. If you need an overview before proceeding, please see the "Neural Networks" box.

Neural Networks

Neural networks were given the name because they imitate the neural connections found in brains in a highly simplified form. In the brain, information is processed by cells that connect with each other depending on experiences and sensations. Machine Learning uses a similar, but extremely simplified, model: Artificial neurons are connected to other artificial neurons in a network. According to

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