Get to know the Fundamentals of Artificial Neural Network – Future Jobs For You (2024)

• Recognize people after so many years
• Read different handwriting
• Can learn!

• The brain has around 10^11 neurons
• Each neuron has up to the 10000 connections

Our sense organs interact with the outer world and send the visual and sound information to the neurons.

By looking at a picture, each neuron in your brain gets fired or activated only when its respective criteria are met.There are millions of neurons interconnected to make a hierarchical decision based on what they receive.

See the below video for an example.

what is classification?

According to the dictionary, classification is an action or a process of classifying something based on shared qualities or characteristics.

But why is it important for us and how is this related to Machine Learning?

The first thing that we all learned in this school was classifying. In a math class, we learned how to classify different shapes into different groups. If a shape has four right angles and four straight lines, then we can call it square. Basically, we are learning how to classify different things.

In science, We also learn how to classify different plants, animals, and we gave each group a name. This is an important aspect of machine learning, as well. A manager who wants to decide about some particular situation will compare the existing situation with one of the sets that he has in his mind to make a suitable decision!

We have expressed the classification of some datasets with a mathematical expression. Now let us model a simple neuron using this expression.

In this expression, ‘x’ and ‘y’ are our inputs to the neuron. Each of these inputs has a particular effect on our system which is presented by ‘a’ and ‘b’. For example, the income of people has more effect on the place that they chose for living than their color of eyes! So we can give more weight to the variable income and less (or non) weight to the color of the eyes variable.

Let’s take a look at Combinational Logic Gate ‘AND’ and use a binary linear classification to plot it on a graph.

We also know the AND gate as a truth table. let’s say we have two statements and want to know if the person is telling us truth or not!

In this table ‘1’ represents true and ‘0’ represents false answer.

We will ask two questions from the target person and record the answers in columns ‘X’ and ‘Y’. If and only if this person is telling a correct answer to both questions (meaning two ‘1’ inputs), then we can trust this person and will mark ‘1’ in the last column ‘Z’.

Now that we defined a simple artificial Neuron, it’s time to take a look at exclusive logical gate XOR.

Here we have two inputs. we can call them A and B. In the logic gate XOR, the output is 1 only when the inputs are different.

As you can see on the graph it is not possible to classify the dataset of XOR using only one linear line, though we must use two linear lines. And remember each linear line represents a neuron, therefore we will need two neurons to design our Artificial Neural Network!

Below figure is a block diagram of a multilayer Perceptron where we have a vector of X for our inputs, and two neurons where the output of the first neuron is called y1 and the output of the second neuron is called y2.

The y1 and y2 signals are fed into another neuron N3 that is acting as the activation function to generate the output Z.

You can simply design your neural network with software and then implement it using hardware, below shows a simple model to represent a neuron. Just watch the below video to learn more.

There are several advantages of using an analog system compared to a digital system. One of them is that analog systems are much faster compared to digital systems, so they can respond promptly. But of course, there are some limitations as well. analog systems are not extensible and we can just use them for a specific purpose.

The first layer, usually in many textbooks, it’s called the input layer. Then whatever the layer is between the input layer and output layer are called the hidden layers. They are called hidden layers because they’re hidden from the output layer and therefore from us!

While designing an artificial neural network, we can adjust the weight of each neuron as well as our bias to meet the desired output.

Here we have two systems, one is an artificial neural network, our model that we want to design to predict a particular output, the other one is just a process, for example, a correlation between two datasets in the stock market.

First we have to feed the same input into both systems, process and the ANN, and get their outputs.

Ideally, both outputs y and y^ must be the same with 0 error so that we can say our ANN has 100% efficiency (which is not possible when we work with natural systems due to several factors affecting the output).

So what we need to do is to take the error between y and y^, then try to minimize this error. Watch the video to understand it better!

To optimize our system we have to minimize the error between the output of ANN and process. There are several ways to do that and one of them is to calculate the mean squared error or MSE which is the average of the squares of the errors.

Watch the below video and learn some math behind it!

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Learn to Create Neural Network with Matlab Toolbox and Easy to Follow Codes; with Comprehensive Theoretical Concepts

This course is uniquely designed to be suitable for both experienced developers seeking to make that jump to Machine learning or complete beginners who don’t understand machine learning and Artificial Neural Network from the ground up.

In this course, we introduce a comprehensive training of multilayer perceptron neural networks or MLP in MATLAB, in which, in addition to reviewing the theories related to MLP neural networks, the practical implementation of this type of network in MATLAB environment is also fully covered.

MATLAB offers specialized toolboxes and functions for working with Machine Learning and Artificial Neural Networks, making it a lot easier and faster for you to develop a NN.

At the end of this course, you’ll be able to create a Neural Network for applications such as classification, clustering, pattern recognition, function approximation, control, prediction, and optimization.