What is TensorFlow 2.0 and what updates and improvements does it include?

TensorFlow2.0 is an open-source machine learning framework and the next major version of the TensorFlow machine learning framework. It aims to improve some shortcomings of TensorFlow 1.x by simplifying the API, enhancing performance, and increasing usability.

Some updates and improvements made to TensorFlow 2.0 include:

1. Eager Execution: With TensorFlow 2.0, Eager Execution is enabled by default, allowing users to execute operations instantly, similar to using Python, without the need to build a computational graph.
2. Keras integration: Keras is now the high-level API for TensorFlow 2.0 and is available by default. Keras simplifies the process of building and training models.
3. Improved model building: TensorFlow 2.0 makes it easier to construct models by providing a more concise and intuitive API.
4. Remove redundant features: TensorFlow 2.0 has removed some outdated and redundant features, simplifying the overall framework.
5. Model saving and loading: TensorFlow 2.0 offers a simpler way to save and load models.
6. Improved performance: TensorFlow 2.0 enhances performance by optimizing the execution of computational graphs.
7. Distributed training: TensorFlow 2.0 offers improved support for distributed training, making it easier to train across multiple devices.

In general, the updates and improvements in TensorFlow 2.0 have made it more user-friendly, efficient, and provided better performance and flexibility.