Data flow graphs are used in TensorFlow, an open-source software library for numerical computing. The graph's nodes stand in for mathematical processes, while its edges stand in for the multidimensional data arrays (tensors) that are transmitted between them. With a single API, you may use the flexible architecture to deploy compute to one or more CPUs or GPUs in a desktop, server, or mobile device. TensorFlow was initially created by scientists and engineers working on the Google Brain Team within Google's Machine Intelligence research organisation to conduct machine learning and deep neural network research, but the system is sufficiently general to be applicable in a wide range of other domains as well.

Major corporations all around the world use it, including Google, Airbnb, eBay, Dropbox, Snapchat, Twitter, Uber, SAP, Qualcomm, IBM, and so on!

The most well-known deep learning library in the world, Tensorflow, was created by Google, whose parent firm Alphabet just overtook Apple as the richest corporation in the world in terms of cash (just a few days before I wrote this). It is the preferred library for many businesses engaged in AI and machine learning.

Because of this, you should really understand TensorFlow given how prevalent and well-liked it is. And to help you learn, we've put up a list of the Best Tensorflow Course you absolutely need to take to become proficient in the subject.

1. Total TensorFlow Guide: Deep Learning with Python Course

2. TensorFlow: Getting Started

3. Tensorflow 2.0: Deep Learning and Artificial Intelligence

4. Understanding the Foundations of TensorFlow

5. Complete Tensorflow 2 and Keras Deep Learning Bootcamp