By allowing the network to learn on its own, we can avoid the necessity of entering in all the rules. In neural networks, you tell your network the inputs and what you want for the outputs, and let it learn on its own. In this approach, you tell the program exactly what you want it to do. First, there’s the specifically guided and hard-programmed approach. There are two different approaches to get a program to do what you want. When you’re training your network, you’re deciding how the weights are adjusted. By adjusting the weights, the ANN decides to what extent signals get passed along.

What about synapses? Each of the synapses gets assigned weights, which are crucial to Artificial Neural Networks (ANNs). (You will need to either standardize or normalize these variables so that they’re within the same range.) This information is broken down into numbers and the bits of binary data that a computer can use.

These are independent variables for one single observation. Think of the input layer as your senses: the things you, for example, see, smell, and feel. So the neuron (or node) gets a signal or signals ( input values), which pass through the neuron. This happens over and over until your final output signal! That layer creates an output which in turn becomes the input for the next layer, and so on. That’s the idea behind a deep learning algorithm! You get input from observation and you put your input into one layer. But when you have lots of them, they work together to create some serious magic. Neurons by themselves are kind of useless.

Machine learning deep learning