Deep Learning with TensorFlow

DNNs architectures

Deep Neural Networks (DNNs) are artificial neural networks strongly oriented to deep learning. Where normal procedures of analysis are inapplicable due to the complexity of the data to be processed, such networks are an excellent modeling tool. DNNs are neural networks, very similar to those we have discussed, but they must implement a more complex model (a great number of neurons, hidden layers, and connections), although they follow the learning principles that apply to all machine learning problems (that is, supervised learning).

As they are built, the DNNs work in parallel, so they are able to treat a lot of data. They are a sophisticated statistical system, equipped with a good immunity to errors.

Unlike algorithmic ...