8. Training Deep Networks

In the preceding chapters, we described artificial neurons comprehensively and we walked through the process of forward propagating information through a network of neurons to output a prediction, such as whether a given fast food item is a hot dog, a juicy burger, or a greasy slice of pizza. In those culinary examples from Chapters 6 and 7, we fabricated numbers for the neuron parameters—the neuron weights and biases. In real-world applications, however, these parameters are not typically concocted arbitrarily: They are learned by training the network on data.

In this chapter, you will become acquainted with two techniques—called gradient descent and backpropagation—that work in tandem to learn artificial neural network ...

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