This practical guide shows how to build a brain with the Keras Functional API in a few lines of code to create powerful deep learning and artificial intelligence models, including training, evaluation, saving, and advanced graph designs
The Keras Functional API allows defining input, hidden, and output layers, connecting them graphically to build customizable architectures adjustable to project requirements, and it is ideal for custom software initiatives and custom applications
Once the model structure is defined, it is compiled by selecting an optimizer, loss function, and metrics; then it is trained with real data, its performance is evaluated, and finally both the weights and the topology are saved for later deployment
For more complex designs, computation graphs can be created that include parallel paths, processing data from different sources, and conditional branches, providing maximum flexibility in artificial intelligence projects
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