How Neural Network Works

how neural network works

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Neural networks reflect the behaviour of the human brain, allowing computer programs to recognize patterns and solve common problems in the fields of Artificial Intelligence, machine learning, and deep learning. Before moving on to “How Neural Network Works”, let’s understand what is a neural network.

What are Neural Networks?

Artificial Neural Networks (ANNs) are layers of nodes, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network.

Based on the human brain, neural networks are used to solve computational problems by imitating the way neurons are fired or activated in the brain. During a computation, many computing cells work in parallel to produce a result. Most neural networks can still operate if one or more of the processing cells fail.

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Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithms are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence. 

Tasks in speech recognition or image recognition can take minutes as compared to hours when compared manually by human experts. One of the most well-known neural networks is Google’s search algorithm.

Architecture of Neural Network

A typical neural network consists of a large number of artificial neurons which are the building blocks of the network. These units are arranged in a series of layers. There are mainly three types of layers present in a neural network.

Input layer

The input layers contain artificial neurons which receive input from the outside world. This is where the actual learning on the network happens, or recognition happens else it will process.

Output layer

The output layers contain artificial neurons that respond to the information that is fed into the system and also whether it learned any task or not.

Hidden layer

The hidden layers are the ones that are present between input and output layers. The only job of a hidden layer is to transform the input into something meaningful that the output layer can use in some way.

Most of the artificial neural networks are all interconnected, which means that each of the hidden layers is individually connected to the neurons in its input layer and also to its output layer leaving nothing to hang in the air. This makes it possible for a complete learning process and also learning occurs to the maximum when the weights inside the artificial neural network get updated after each iteration.

How Neural Network Works
Neural Network Architecture

How does a Neural Network work?

Let’s consider a neural network as shown in the figure. It has an input layer, hidden layer, and output layer. For simplicity, only one layer of the hidden layer is considered. Real networks can be much more complex with several additional layers. Deep Learning gets its name from the fact that there are several hidden layers, in a sense increasing the “depth” of the neural network.

How Neural Network Works
Simple Neural Network

The neural network receives an example and guesses the answer. If the answer is wrong, it goes back and changes the weights and biases in the neurons and tries to correct the error by changing some values. This process is called backpropagation and it simulates what people do when performing a task using an iterative trial and error approach.

After doing this process several times, the neural network begins to improve (learn) and provide better responses. Each of these iterations is called an epoch. Sometimes it takes days or weeks to provide training to learn complex tasks. Once the neural network achieves accuracy the topology of the network is copied with little or no changes. Now you get a trained neural network, which can be used over and over again until you need something different. Then it’s back to the training mode.

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