NNs with Keras

Knowing, at least intuitively, the components of a deep learning model and how they interact is a must before going any further into practical details. The practical details might change with respect to which deep learning framework and API to be used; this chapter uses Keras. It will give access to Google's TensorFlow and some other frameworks.

Keras is the ancient Greek word for horn, which makes reference to Odyssey, written by Homer. In his narrative, the spirits that came from a gate made of polished horn had fulfilling visions and accurate predictions.

Building cutting-edge models using Keras is easy. The workflow usually goes as follows: once you are done with data preprocessing, you design the network's architecture and choose a learning strategy, a cost function, and measures to track. The next steps are training and testing.

The process of designing a new network is rarely linear such as the one just described. Going back and forth is almost inevitable if you want to improve a network.

Four words could be used to summarize (usual) workflow: design, compile, train, and test. Although the ANN models are usually very complex, to build a very good one from scratch using Keras is very simple; it skips a bunch of human steps without losing flexibility. First things first, the environment must be properly setup.

Table of Contents for NNs with Keras

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Table of Contents for
NNs with Keras