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Deep Learning: Understanding Artificial Neural Network

Over the past three to four years, there has been an evolving breakthrough in the world of technology. This has put into great knowledge on how powerful machines can become in making decisions based completely on facts and figures that have been around for centuries‐ a feat not totally possible with any amount of human effort.

This movement in the world of technology (understanding of data) has led to many studies that individually make dramatic progress in improving the world. One of these areas is what has been known as Deep Learning. But what exactly is it? Well, let’s try to find out.

Deep learning itself is part of an even larger field of study and a machine for learning or research at a higher level. Deep learning is based on highly sophisticated algorithms that operate in a structure whose structure and the concept is entirely derived from and synonymous with the brain of the human body.

As such, it is well understood that the heart of these frames must be similar to neurons in different ways, just as neurons are the heart of our entire nervous system. This structure as a whole is what we call an artificial neural network (abbreviated ANN).

Features of Artificial Neural Network

It is these same neural networks that are responsible for breakthroughs in the field of artificial learning and machine learning. These nets are slow at the time of conception, just like the mind of a newborn completely devoid of consciousness and unaware of how the world works.

Exposing them to real data ( facts and figures) is what makes their precision more accurate to accomplish the highly sophisticated and advanced tasks entrusted to them.

Like the human brain, these neural networks work best when they learn from real‐life experiences. Once the network and its associated model reach the desired level of accuracy, it’s really fun and intriguing to see them at work.

Artificial neural networks are a major aspect of deep learning. In theory, ANN can be defined and visualized as several interconnected (artificial) neurons that exchange data with each other.

If the meaning and comprehensibility of these data are more than the acquired experience of a neuron, the neuron will be updated in terms of knowledge and experience, and if, on the contrary, the neuron processes the data as a function of his experience and returns a result.

How does Artificial Neural Network work?

Relating ANN to music, here, the input corresponds to the musical notes and the output that the name of the music has just recognized; the same thing is applicable to Artificial Neutral Network and there are 3 steps to it. Input, Hidden layers, and Output layers. However, a single note will not be enough to recognize an entire melody.

The ARN, therefore, needs more input data to learn before it can provide a valid output. Web connections in RNA are organized in layers, and a layer contains one to several neurons.

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So, for the problem of music, the distribution of the layer is as follows: An input layer containing information for the ANN network learns, for example, the musical notes in which each note is a neuron; one to several hidden layers that connect the input information to the output; an output layer to give the answers; in this case, yes / no, if the musical notes correspond to a given song.

How does ANN learn?

ANN learns by iterations or repetitions, and these iterations are called times. Thus, for each ANN learning season, there are: Feed input data; Spread the signal through the layers; Take a step…

Well, then, if we do not tell the network when it stops, the loop can continue indefinitely. This flow needs to be further elaborated by defining stopping conditions somewhere, at a given time, when it is certain that the network has learned.

As in the biological model, neurons transmit electrical impulses through layers of neurons in the brain until the desired result is achieved. The best‐known ANN model is multilayered retro‐proposition or multilayer perceptron, and a perceptron is simply a learning neuron.

Let’s further develop the learning model by creating a stopping condition called the desired minimal error (ANN learns from its errors as we do!).

• Feed the input data.

• Spread the signal through the layers from the last output layer to the first hidden layer. This is backpropagation.

• Calculate the current error.

• Ask: Is the current error less than the desired minimum error? Then go out and get out.

• If the current error is higher: return to 1.

This model is still a very simple model, as one might ask: what happens if the current error is never less than the desired minimum error? Then we can create a second stop condition, the maximum number of iterations (times) allowed.

In the second step (backpropagation), some necessary mathematical calculations are made to find the current error. These calculations are based on the connections between the layers.

I will not go into the details of the formulas but I will give the idea behind this: My actual layer data = the calculations of my previous layer. And the previous word is very important here because it’s the way the layers are connected to each other.


So far we’ve talked about neurons, networks, layers, input and output, backpropagation, and time. These words are the usual terminology used in ANN. After these very simple explanations, artificial intelligence is in your hands and you can move on.