What’s New in Tensorflow 2.0?
Introduction
If you are a Machine Learning Engineer, Data Scientist, or a hobbyist developing Machine Learning Models from time to time just for fun, then it is very likely that you are familiar with Tensorflow.
Tensorflow is an open-source and a free framework developed by Google Brain Team written in Python, C++, and CUDA. It is used to develop, test, and deploy Machine Learning models.
Initially, Tensoflow did not have full support for multiple platforms and programming languages, and it was not very fast and efficient for training Machine Learning models, but with time and after a few updates, Tensorflow is now considered as a go-to framework for developing, training and deploying machine learning models.
Tensorflow 1.x
Tensorflow 1.x was also a huge leap for this framework. It introduced many new features, improved performance, and open source contributions. It introduced a high-level API for TensorFlow, which made it very easy to build prototypes in no time.
It was made compatible with Keras. But the major thing that irritated the developers was that it did not feel like taking advantage of the simplicity of Python when using TensorFlow.
In TensorFlow, every model is represented as a graph, and