The main difference between a TensorFlow backend and a Theano backend in Keras is the way in which they handle computations. TensorFlow is a symbolic math library that uses data flow graphs to represent computations, while Theano is a numerical computation library that uses multi-dimensional arrays to represent computations.
TensorFlow is optimized for large-scale computations and is well-suited for deep learning applications, while Theano is optimized for smaller-scale computations and is better suited for more traditional machine learning applications.
TensorFlow also has better support for distributed computing, which makes it easier to scale up computations across multiple machines. Theano, on the other hand, is more focused on optimizing the speed of computations on a single machine.
In terms of usability, TensorFlow is generally considered to be easier to use than Theano, as it has a more intuitive API and better documentation. Theano, however, is more flexible and allows for more customization.
Overall, the choice of backend depends on the type of application you are developing and the type of computations you need to perform. If you are developing a deep learning application, then TensorFlow is likely the better choice. If you are developing a more traditional machine learning application, then Theano may be the better choice.
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