TensorFlow is an open-source software library for machine and deep learning. It was created by the Google Brain Team and has a wide range of uses, such as neural networks for computer vision, natural language processing, and self-driving vehicles.
With high performance and efficiency, TensorFlow enables users to construct, optimize, and assess mathematical expressions involving multi-dimensional arrays. Researchers and developers choose it because it offers a versatile and high-level API for building, honing, and deploying machine learning models. Additionally, TensorFlow has a sizable and expanding contributor community that offers a plethora of tools, tutorials, and resources for users to employ.
In order to create your first deep-learning model using Tensorflow, you really don't need to know much about machine learning, but if you really want to know what happens in the background and how machine-learning models work, you need to learn a little about different machine learning methods and have some mathematical background. Those topics are not really the scope of this article, and I will just try to explain some basic concepts as we are creating our model to familiarize you with the terminology.
