What are neural network ? :
A Neural Network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature. Neural networks can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria.
Neural networks have a unique ability to extract meaning from imprecise or complex data to find patterns and detect trends that are too convoluted for the human brain or for other computer techniques. Neural networks have provided us with greater convenience in numerous ways, including through ridesharing apps, Gmail smart sorting, and suggestions on Amazon.
A neural network breaks down the input into layers of abstraction. It can be trained using many examples to recognize patterns in speech or images, for example, just as the human brain does. Its behavior is defined by the way its individual elements are connected and by the strength, or weights, of those connections. These weights are automatically adjusted during training according to a specified learning rule until the artificial neural network performs the desired task correctly.
How a Neural Network Works? :
A neural network has many layers. Each layer performs a specific function, and the complex the network is, the more the layers are. That’s why a neural network is also called a multi-layer perceptron.
The purest form of a neural network has three layers:
- The input layer
- The hidden layer
- The output layer
As the names suggest, each of these layers has a specific purpose. These layers are made up of nodes. There can be multiple hidden layers in a neural network according to the requirements. The input layer picks up the input signals and transfers them to the next layer. It gathers the data from the outside world.
The hidden layer performs all the back-end tasks of calculation. A network can even have zero hidden layers. However, a neural network has at least one hidden layer. The output layer transmits the final result of the hidden layer’s calculation.
Like other machine learning applications, you will have to train a neural network with some training data as well, before you provide it with a particular problem. But before we go more in-depth of how a neural network solves a problem.
Types of Neural Networks :
There are many types of neural networks available or that might be in the development stage. They can be classified depending on their: Structure, Data flow, Neurons used and their density, Layers and their depth activation filters etc.
1) Recurrent Neural Network (RNN)
In this network, the output of a layer is saved and transferred back to the input. This way, the nodes of a particular layer remember some information about the past steps. The combination of the input layer is the product of the sum of weights and features. The recurrent neural network process begins in the hidden layers.
2) Convolutional Neural Network (CNN)
This network consists of one or multiple convolutional layers. The convolutional layer present in this network applies a convolutional function on the input before transferring it to the next layer. Due to this, the network has fewer parameters, but it becomes more profound. CNNs are widely used in natural language processing and image recognition.
3) Feedforward Neural Network (FNN)
This is the purest form of an artificial neural network. In this network, data moves in one direction, i.e., from the input layer to the output layer. In this network, the output layer receives the sum of the products of the inputs and their weights. There’s no back-propagation in this neural network. These networks could have many or zero hidden layers. These are easier to maintain and find application in face recognition.
Real-World and Industry Applications of Neural Networks :
- Aerospace: Aircraft component fault detectors and simulations, aircraft control systems, high-performance auto-piloting, and flight path simulations
- Automotive: Improved guidance systems, development of power trains, virtual sensors, and warranty activity analyzers
- Electronics: Chip failure analysis, circuit chip layouts, machine vision, non-linear modeling, prediction of the code sequence, process control, and voice synthesis
- Manufacturing: Chemical product design analysis, dynamic modeling of chemical process systems, process control, process and machine diagnosis, product design and analysis, paper quality prediction, project bidding, planning and management, quality analysis of computer chips, visual quality inspection systems, and welding quality analysis
- Mechanics: Condition monitoring, systems modeling, and control
- Robotics: Forklift robots, manipulator controllers, trajectory control, and vision systems
- Telecommunications: ATM network control, automated information services, customer payment processing systems, data compression, equalizers, fault management, handwriting recognition, network design, management, routing and control, network monitoring, real-time translation of spoken language, and pattern recognition (faces, objects, fingerprints, semantic parsing, spell check, signal processing, and speech recognition).
CONCLUSION :
Neural computers perform very favorably in business and military applications. They do not require explicit programming by an expert and are robust to noisy, imprecise or incomplete data.
Furthermore, knowledge is encapsulated in a compact, efficient way that can easily be adapted to changes in business environment. As with all technologies, there is a window of opportunity for exploitation-and that window is here today.
You cannot afford to ignore the fact that your competitors are already investigating the opportunities and realising the significant business benefits that neural technology brings to a range of applications.
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