How Do Artificial Neural Networks Learn? – Towards Data Science

This is the fifth post (post1, post2, post 3, post 4) in the series that I am writing based on the book First contact with DEEP LEARNING, Practical introduction with Keras. In it I will present an intuitive vision of the main components of the learning process of a neural network and put into practice some of the concepts presented here with an interactive tool called TensorFlow Playground. Remember that a neural network is made up of neurons connected to each other; at the same time, each connection of our neural network is associated with a weight that dictates the importance of this relationship in the neuron when multiplied by the input value. Each neuron has an activation function that defines the output of the neuron. The activation function is used to introduce non-linearity in the modeling capabilities of the network. We have several options for activation functions that we will present in this post. Training our neural network, that is, learning the values of our parameters (weights wij and bj biases) is the most genuine part of Deep Learning and we can see this learning process in a neural network as an iterative process of "going and return" by the layers of neurons. The "going" is a forwardpropagation of the information and the "return" is a backpropagation of the information. The first phase forwardpropagation occurs when the network is exposed to the training data and these cross the entire neural network for their predictions (labels) to be calculated.