Keras follows the best practices associated with reducing cognitive load. It offers simple and consistent APIs and affords us the freedom to design our own architecture.
Keras provides clear feedback on user error, which minimizes the number of user actions required. It provides high flexibility as it integrates with lower-level deep learning languages such as TensorFlow. You can implement anything that was built in the base language.
Keras also supports various programming languages. We can develop Keras in Python, as well as R. We can also run the code with TensorFlow, CNTK, Theano, and MXNet, which can be run on the CPU, TPU, and GPU as well. The best part is that it supports both NVIDIA and AMD GPUs. These advantages offered by Keras ensure that producing models with Keras is really simple. It can run with TensorFlow Serving, GPU acceleration (web Keras, Keras.js), Android (TF, TF Lite), iOS (Native CoreML), and Raspberry Pi.
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