Primarily, there are two levels of abstraction for deep learning frameworks:
- Firstly, there is the lower level, where frameworks such as TensorFlow, Theano, and PyTorch sit. It is at this level where neural network elements such as convolutions and other generalized matrix operations are carried out.
- Then, there is a higher level, where frameworks such as Keras are present. Here, primitives from the lower levels are utilized to create neural network layers and models. User-friendly APIs for training and saving models are also implemented here.
Since they are present on different levels of abstraction, you cannot compare Keras and TensorFlow. TensorFlow, while being used for deep learning, is not a dedicated deep learning library and is used for a wide array of other applications besides deep learning. Keras, however, is a library developed from the ground up specifically for deep learning. It has very well-designed APIs...
