How Neural Networks Work – Architecture & Working

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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.

Let’s understand what is a neural network and how neural networks work.

Neural Network Meaning

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.

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.

Why are Neural Networks Important?

Neural networks are also ideally suited to help people solve complex problems in real-life situations. They can learn and model the relationships between inputs and outputs that are nonlinear and complex; make generalizations and inferences; reveal hidden relationships, patterns, and predictions; and model highly volatile data (such as financial time series data) and variances needed to predict rare events (such as fraud detection).

Types of Neural Networks

There are different kinds of deep neural networks – and each has advantages and disadvantages, depending upon the use. Examples include:

• Convolutional Neural Networks (CNNs) contain five types of layers: input, convolution, pooling, fully connected, and output. Each layer has a specific purpose, like summarizing, connecting, or activating. Convolutional neural networks have popularized image classification and object detection. However, CNNs have also been applied to other areas, such as natural language processing and forecasting.
• Recurrent Neural Networks (RNNs) use sequential information such as time-stamped data from a sensor device or a spoken sentence, composed of a sequence of terms. Unlike traditional neural networks, all inputs to a recurrent neural network are not independent of each other, and the output for each element depends on the computations of its preceding elements. RNNs are used in fore­casting and time series applications, sentiment analysis, and other text applications.
• Feedforward Neural Networks (FNNs), in which each perceptron in one layer is connected to every perceptron from the next layer. Information is fed forward from one layer to the next in the forward direction only. There are no feedback loops.
• Autoencoder Neural Networks are used to create abstractions called encoders, created from a given set of inputs. Although similar to more traditional neural networks, autoencoders seek to model the inputs themselves, and therefore the method is considered unsupervised. The premise of autoencoders is to desensitize the irrelevant and sensitize the relevant. As layers are added, further abstractions are formulated at higher layers (layers closest to the point at which a decoder layer is introduced). These abstractions can then be used by linear or nonlinear classifiers.

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 the 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 artificial neural networks are 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 Networks Work?

A node is patterned after a neuron in the human brain. Similar in behavior to neurons, nodes are activated when there are sufficient stimuli or input. This activation spreads throughout the network, creating a response to the stimuli (output). The connections between these artificial neurons act as simple synapses, enabling signals to be transmitted from one to another. Signals across layers as they travel from the first input to the last output layer – and get processed along the way.

When posed with a request or problem to solve, the neurons run mathematical calculations to figure out if there’s enough information to pass on the information to the next neuron. Put more simply, they read all the data and figure out where the strongest relationships exist. In the simplest type of network, data inputs received are added up, and if the sum is more than a certain threshold value, the neuron “fires” and activates the neurons it’s connected to.

As the number of hidden layers within a neural network increases, deep neural networks are formed. Deep learning architectures take simple neural networks to the next level. Using these layers, data scientists can build their own deep learning networks that enable machine learning, which can train a computer to accurately emulate human tasks, such as recognizing speech, identifying images, or making predictions. Equally important, the computer can learn on its own by recognizing patterns in many layers of processing.

So let’s put this definition into action. Data is fed into a neural network through the input layer, which communicates to hidden layers. Processing takes place in the hidden layers through a system of weighted connections. Nodes in the hidden layer then combine data from the input layer with a set of coefficients and assign appropriate weights to inputs. These input-weight products are then summed up. The sum is passed through a node’s activation function, which determines the extent that a signal must progress further through the network to affect the final output. Finally, the hidden layers link to the output layer – where the outputs are retrieved.

Practice Problems

1. What is a Neural Network?
2. What are the different types of neural networks?
3. What are the three different layers of a neural network?
4. How Do Neural Networks Work?

FAQs

Conclusion

Neural networks, usually simply called neural networks or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. Neural networks are computing systems with interconnected nodes that work much like neurons in the human brain. Using algorithms, they can recognize hidden patterns and correlations in raw data, cluster and classify it, and – over time – continuously learn and improve.

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