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What are neurons An artificial neuron is a mathematical function conceived as a model of biological neurons, a neural network.

In this article we will not be exploring the advanced mathematical concepts and formulas we will be looking at more of a general overview of the topic with a basic example.

Artificial Neural Networks have been a round since the 1940s but never worked efficiently, but recently they have become extremely powerful and are one of the most popular machine learning models because of its results which no other model can come close to.

Neural Networks are very powerful when you have massive datasets.

This means that the neural network has enough data to create statistical models of the data which has been inputted, this is why they have been becoming more and more successful because of the amount of new data coming out every year.

What is an Artificial Neural Network A good place to start would be learning what an Artificial Neural Network is and what is does.

An Artificial Neural Network is an information processing model that is inspired by the way biological nervous systems, such as the brain, process information.

They are loosely modeled after the neuronal structure of the mamalian cerebral cortex but on much smaller scales.

What are Artificial Neural Networks used for Artificial Neural Networks are used for a variety of tasks, a popular use is for classification.

Neural Networks are also used in Self Driving cars, Character Recognition, Image Compression, Stock Market Prediction, and lots of other interesting applications.

How does a neural network learn This is a very simple example of a neural network The Artificial Neural Networks ability to learn so quickly is what makes them so powerful and useful for a variety of tasks.

But how do they learn Information flows through a neural network in two different ways.

Each neuron receives inputs from the neurons to its left, and the inputs are multiplied by the weights of the connections they travel along.

Every neuron adds up all the inputs it receives in this way and (this is the simplest neural network) if the sum is more than a certain threshold value, the neuron fires and triggers the neurons its connected to (the neurons on its right).

For an artificial neural network to learn, it has to learn what it has done wrong and is doing right, this is called feedback.

Feedback is how we learn what is wrong and right and this is also what an artificial neural network needs for it to learn.

This is where you start to see similarities to the human brain.

If you are learning to play a game like tennis you learn that if you hit the ball too hard it will go out of the court and you will lose the point, or if you dont hit the ball hard enough it wont go over the net but if you hit it perfectly it will go onto the other side in the court and if could win a point, this is a classic example of feedback where you lose the point or potentially gain a point.

This is how we learn what we are doing correct or wrong and this is what a neural network needs to learn.

Neural networks learn things in exactly the same way as the brain, typically by a feedback process called back-propagation (this is sometimes shortened to backprop).

This is where you compare the output of the network with the output it was meant to produce, and using the difference between the outputs to modify the weights of the connections between the neurons in the network, working from the output units through the hidden neurons to the input neurons going backward.

Over time, back-propagation causes the network to learn by making the gap between the output and the intended output smaller to the point where the two exactly match, so the neural network learns the correct output.

Artificial Neural Network Ppt