Inside a human brain, there is an intensely dense and complex inter-connection of neurons present - which I have already mentioned in the previous article: “The Idea of Deep Learning”.
This I believe couldn’t get any more fascinating than it is. The brain cannot see, hear, touch, smell or taste things out there in the world, it just receives signals and transmits them, as if it is enclosed in a black box (the bones, flesh and skin). And yet it is responsible for all our senses, thoughts, recognition and conscience. All these by individual functioning (transmission of electrical impulses) of the neurons in an intricately complex inter-connected network.
This is how a neuron actually looks like,
Dendrites are the receptors, they receive signals (electrical impulses) from other neurons, these signals gets processed in the nucleus of the neuron and then is passed through the axon and is transmitted to the other neurons via neurotransmitters across the synapses.
An artificial neural network is basically a model imagined or we can say inspired from the human brain. Let me clarify that the neural networks in our brain are not as simple and much complex than the artificial neural network.
The one shown above is a single neuron. Trillions and zillions of such neurons are inter-connected in an intricately complex way inside our brain. Although we can construct a complex neural network but the neural architecture of our brain is much more complex and yet unexplored.
The same concept of the network of neurons is used in machine learning algorithms. In this case , the neurons are created artificially on a computer . Connecting many such artificial neurons creates an artificial neural network. The working of an artificial neuron is similar to that of a neuron present in our brain.
You must have seen images like the one above before, or for that matter in the previous article.
And, I am sure you must be wondering, how is this even supposed to make any sense? And how the heck does a neuron actually work? What’s inside it? Well, this is where this subject gets even more fascinating.
To get the an idea, let’s zoom in to a single neuron in a network.
The diagram shown above is a single layer neuron (also called a perceptron).
The input feeds to the neuron are like the dendrites and the output to z’ is like an axon in an actual neuron.
The entire functioning of a neuron is processed mathematically, i.e, the actual biological neurons are simulated via mathematical functions. Or simply put, the entire human brain functioning can be mapped using a network of selectively triggered mathematical functions. How can it get any more fascinating? I mean it blew my mind at first when I realized this.
There are several categories of learning algorithms for the neurons to learn from. The most popular among these are:
The activation functions inside the neurons can be imagined as the small knobs and regulators that can turned and adjusted to tune the input-output mapping (the function generated). There are numerous activation functions out there to choose from, according to our requirements. However I will be discussing four most pre dominant ones in this article.
Hi, I am Subham, I am a learning enthusiast, an active reader and the author of this article. Hope you liked this article.