Hands-On Deep Learning for IoT

Training a neural network

The learning process for a neural network is configured as an iterative process of the optimization of the weights. The weights are updated in each epoch. Once the training starts, the aim is to generate predictions by minimizing the loss function. The performance of the network is then evaluated on the test set. We already know about the simple concept of an artificial neuron. However, generating only some artificial signals is not enough to learn a complex task. As such, a commonly used supervised learning algorithm is the backpropagation algorithm, which is very often used to train a complex ANN.

Ultimately, training such a neural network is an optimization problem, too, in which we try to minimize the error by ...