Deep learning is a machine learning technique.
It teaches a computer to filter inputs through layers to learn how to predict and classify information.
Neurons by themselves are kind of useless. But when you have lots of them, they work together to create some serious magic.
That connection where the signal passes is called a synapse.
So the neuron (or node) gets a signal or signals (input values), which pass through the neuron. That neuron delivers the output signal. Think of the input layer as your senses: the things you, for example, see, smell, and feel. These are independent variables for one single observation. This information is broken down into numbers and the bits of binary data that a computer can use. (You will need to either standardize or normalize these variables so that they’re within the same range.)
What about synapses? Each of the synapses gets assigned weights, which are crucial to Artificial Neural Networks (ANNs). Weights are how ANNs learn. By adjusting the weights, the ANN decides to what extent signals get passed along. When you’re training your network, you’re deciding how the weights are adjusted.