What Is a Neural Network?
A neural network (also called an artificial neural network) is an adaptive system that learns by using interconnected nodes or neurons in a layered structure that resembles a human brain. A neural network can learn from data — so it can be trained to recognize patterns, classify data, and forecast future events.
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.
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.
How Do Neural Networks Work?
A neural network combines several processing layers, using simple elements operating in parallel and inspired by biological nervous systems. It consists of an input layer, one or more hidden layers, and an output layer. In each layer, there are several nodes, or neurons, with each layer using the output of the previous layer as its input, so neurons interconnect the different layers. Each neuron typically has weights that are adjusted during the learning process, and as the weight decreases or increases, it changes the strength of the signal of that neuron.
Artificial Neural Networks (ANN) and Biological Neural Networks (BNN) — Difference
Application of Neural Networks
Human Face Recognition
It is one of the biometric methods to identify the given face. It is a typical task because of the characterization of “non-face” images. However, if a neural network is well trained, then it can be divided into two classes namely images having faces and images that do not have faces.
First, all the input images must be preprocessed. Then, the dimensionality of that image must be reduced.
Speech occupies a prominent role in human-human interaction. Therefore, it is natural for people to expect speech interfaces with computers. In the present era, for communication with machines, humans still need sophisticated languages which are difficult to learn and use. To ease this communication barrier, a simple solution could be, communication in a spoken language that is possible for the machine to understand.
The neural network will recognize handwritten characters Compared with most of the current text recognition systems, which extract all the information from the image at once, STN-OCR behaves more like a human. First, STN-OCR localizes text regions and then recognizes the textual content of each text region. To do this, STN-OCR consists of two stages: text detection and text recognition.
Deep Learning for Business: Use Cases
Financial Services Industry
Neural networks are helping financial services firms to automate increasingly complex processes and decisions that can ultimately lower costs, improve accuracy and customer experience, and give businesses a competitive edge.
There are many opportunities for applying deep learning technology in the financial services industry. One important task that deep learning can perform is e-discovery. For example, large investment houses like JPMorgan Chase are using deep learning-based text analytics for insider trading detection and government regulatory compliance. Hedge funds use text analytics to drill down into massive document repositories for obtaining insights into future investment performance and market sentiment. The use case for deep learning-based text analytics revolves around its ability to parse massive amounts of text data to perform analytics or yield aggregations.
Deep learning also has a number of use cases in the cybersecurity space. One of the advantages that deep learning has over other approaches is accuracy. In many cases, the improvement approaches a 99.9% detection rate. The high risk and cost associated with not detecting a security threat make the expense related to deep learning justified.
Deep learning can play a number of important roles within a cybersecurity strategy. Use cases include automating intrusion detection with an exceptional discovery rate. Deep learning also performs well with malware, as well as malicious URL and code detection. Deep learning for cybersecurity is a motivating blend of practical applications along with untapped potential. With proper vetting, it’s well worth the effort to ensure the time and investment required for implementing a solution that yields the anticipated gains.
Deep learning’s power can also be seen in how it’s being used in social media technology. Let’s take Pinterest for example, which includes a visual search tool that lets you zoom in on a specific object in a “Pin” (or pinned image) and discover visually similar objects, colors, patterns, and more.
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. You cannot afford to ignore the fact that your competitors are already investigating the opportunities and realizing the significant business benefits that neural technology brings to a range of applications.
The reason one should use neural computing technology is the competition!