TensorFlow 2.0: Key Features & Optimizations

TensorFlow 2.0 is a major update released by the TensorFlow team, with the following key features and optimizations:

1. In TensorFlow 2.0, Eager Execution mode is enabled by default, allowing users to execute and debug models instantly like regular Python code, without the need to construct a computational graph.
2. Keras serves as the primary API: TensorFlow 2.0 has integrated Keras as its main API, making model creation, training, and debugging easier and more intuitive.
3. Support for various hardware accelerators: TensorFlow 2.0 supports multiple hardware accelerators, such as GPUs and TPUs, enabling more efficient utilization of hardware resources for model training and inference.
4. Improved model building and training API: TensorFlow 2.0 offers a more concise, flexible, and user-friendly API for constructing and training models, making it easier for users to build and train complex deep learning models.
5. Improved performance optimization: TensorFlow 2.0 has been optimized for better performance, including speeding up model training and inference, reducing memory usage, and other optimizations.

Overall, TensorFlow 2.0 has made significant improvements in simplifying the API, enhancing performance, and increasing flexibility, allowing users to more easily build, train, and deploy deep learning models.