7 Steps to Mastering Deep Learning with Keras

There is no shortage of neural network frameworks, libraries, and APIs available to anyone interested in getting started with deep learning. Keras is a high-level neural network API, helping lead the way to the commoditization of deep learning and artificial intelligence. It runs on top of a number of lower-level libraries, used as backends, including TensorFlow, Theano, CNTK, and PlaidML. Keras code is portable, meaning that you can implement a neural network in Keras using Theano as a backened and then specify the backend to subsequently run on TensorFlow, and no further changes would be required to your code. We have had access to these algorithms for over 10 years. To me, the most important advances have come with Google Keras, which has commoditized very powerful, modern, AI algos that previous were not only inaccessible bit [sic] thought to be unusable as well.