use Cases of 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. The concept of neural networks, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems.
Basics of Neural Networks
Neural networks, in the world of finance, assist in the development of such process as time-series forecasting, algorithmic trading, securities classification, credit risk modeling and constructing proprietary indicators and price derivatives.
A neural network works similarly to the human brain’s neural network. A “neuron” in a neural network is a mathematical function that collects and classifies information according to a specific architecture. The network bears a strong resemblance to statistical methods such as curve fitting and regression analysis.
A neural network contains layers of interconnected nodes. Each node is a perceptron and is similar to a multiple linear regression. The perceptron feeds the signal produced by a multiple linear regression into an activation function that may be nonlinear.
In a multi-layered perceptron (MLP), perceptrons are arranged in interconnected layers. The input layer collects input patterns. The output layer has classifications or output signals to which input patterns may map. For instance, the patterns may comprise a list of quantities for technical indicators about a security; potential outputs could be “buy,” “hold” or “sell.”
Hidden layers fine-tune the input weightings until the neural network’s margin of error is minimal. It is hypothesized that hidden layers extrapolate salient features in the input data that have predictive power regarding the outputs. This describes feature extraction, which accomplishes a utility similar to statistical techniques such as principal component analysis.
Applications of Neural Networks
Neural networks are broadly used, with applications for financial operations, enterprise planning, trading, business analytics and product maintenance. Neural networks have also gained widespread adoption in business applications such as forecasting and marketing research solutions, fraud detection and risk assessment.
A neural network evaluates price data and unearths opportunities for making trade decisions based on the data analysis. The networks can distinguish subtle nonlinear interdependencies and patterns other methods of technical analysis cannot. According to research, the accuracy of neural networks in making price predictions for stocks differs. Some models predict the correct stock prices 50 to 60 percent of the time while others are accurate in 70 percent of all instances. Some have posited that a 10 percent improvement in efficiency is all an investor can ask for from a neural network.1
There will always be data sets and task classes that a better analyzed by using previously developed algorithms. It is not so much the algorithm that matters; it is the well-prepared input data on the targeted indicator that ultimately determines the level of success of a neural network.
Autonomous vehicles aren’t just a dream anymore. Albeit most of them are still just prototypes, they’re definitely a reality nowadays. Dozens of different companies have already invested a substantial amount of money to fuel this technology.
Deep learning algorithms are employed by software developers to power computer vision, understand all the details about their surrounding environment, and make smart, human-like decisions. For years, human-driven cars have been equipped with an array of cameras and sensors that record everything from driving patterns to road obstacles, traffic lights, and road signs. Now, all this data is used to “teach” the autonomous systems how to recognize these objects and how to appropriately react to external stimuli while driving on a real road.
The idea of using artificial intelligence to optimize the efficiency of networks and improve their security dates back to the early ’80s. However, modern technologies have made a huge leap forward, and revolutionary machine learning algorithms can mundanely perform complex tasks such as predicting faults and scheduling fixes.
AI is exceptionally efficient at allocating network resources where they’re most needed by autonomously analyzing traffic data, and they possess the agility required to integrate themselves with the many internet of things (IoT) devices connected to the network architecture. No one can talk to a machine better than another machine, after all.
ANNs can also be used to protect organizations from several types of attacks, such as DDoS and malicious software. Malware itself is a huge problem, with at least 325,000 new malicious files being generated every day. Yet, no more than 10 percent of the files change from iteration to iteration, so algorithm-based learning models that can predict these variations are able to detect which files are malware with amazing accuracy.
AI is better than humans at cybersecurity because they automate the most complex processes required for detecting attacks and analyzing the best way to react to breaches. More in general, neural nets could be used to detect any change or anomaly in network traffic, including the newest 5G networks. AI can avoid the risk of false positives and identify potentially malicious activities such as brute-force attacks, unusual failed logins and file exfiltration with some experiments reaching a 96.4% detection rate.
Obviously, hackers started developing their own adaptive AI to deceive security software and exploit vulnerabilities, in a never-ending arms race between attackers and defenders. However, all of this actually benefits AIs, which get smarter and smarter every day they are deployed in the battlefield.