Why choose TensorFlow over Keras for deep learning?
The question is about Tensorflow
Choosing to use TensorFlow for deep learning is advantageous when more control, customization, and scalability are needed. While Keras has an easy-to-use high-level API, good to go with for rapid prototyping or simple models, TensorFlow allows the implementation of low-level operations, thereby allowing comprehensive tuning of complex models and advanced configuration. The rich ecosystem around TensorFlow supports native distributed training, GPU/TPU acceleration, and tools for deployment such as TensorFlow Serving and TensorFlow Lite, therefore making it more suitable for production at scale. It is also preferred by developers in the cases where freedom is needed either to optimize performance or to integrate with other tools, because of the comprehensive capabilities it has beyond model building.
