Biological inspiration 

As we mentioned earlier, deep learning is inspired by the human brain. This seems a good idea indeed. To develop the intelligence of the brain inside a machine, you need the machine to mimic the brain! Now, if you are slightly aware of how a human brain learns and memorizes things so fast, you must know that this is possible due to millions of neurons developing an interconnected network, sending signals to each other, which makes up the memory. The neuron has two major components: dendrite and axon. The dendrite acts as a receptor and combines all the signals that the neuron is receiving. The axon is connected to dendrites at the end of other neurons through synapses. Once the incoming signals cross a threshold, they flow through the axon and synapse to pass the signal to the connected neuron. The structure in which the neurons are connected to each other decides the network's capabilities. Following is a diagram of what a biological neuron might look like:

Hence, the artificial model of neural network should be a parallel network of interconnected nodes, which take in inputs from various other nodes, and pass on the output when activated. This activation phenomenon must be controlled by some sort of mathematical operations. Let's see the operations and equations next!