Neural network has all to do with building a computer that functions like a brain. By creating a simplified simulation of the brain process data, you can build a type of computer robots that functions very differently from an ordinary one. Computer processors process data in order.
They perform many operations on a set of data, one at a time. Parallel processing significantly speeds up the computer by using multiple processors in series. Therefore, neural networks through machine learning mechanisms can essentially design their own algorithms that ensure they perform correctly.
Effects of Neural Networks on Cryptocurrency
Cryptocurrencies have become one of the most common digital currencies in our world. Many have invested and got great benefits while others are still weighing the benefits. From many financial analysts, cryptos are the future of the finance world although that stands to be tested by time. Nevertheless, when the performance of cryptocurrencies is mingled with neural networks, it will, perhaps, be the future of the finance world. With the emergence of these robots in the world of crypto, many investors are more confident in investing on these financial assets.
The first cryptocurrency, Bitcoin was designed to work as medium of exchange. Later alternative cryptocurrencies called altcoins emerged and became favorites of many investors. Now the investors from the former existing financial markets are interested in cryptocurrencies as a new financial product on the cryptocurrency market. Cryptocurrencies are interesting for investors due to the diversification effect as they are uncorrelated with each other and uncorrelated with traditional asset.
Neural network robots have been helpful in cryptocurrencies in the following disciplines:
- In predicting prices – it is the dream of every investor to know whether a certain crypto is rising or falling. Neural networks gather the information that affect a certain crypto and predict what the effects would be on its prices.
- In giving a detailed data analysis – sometimes an investor may want to know more about a certain crypto before investing. Neural networks help him with detailed information about that product.
- In showing the trend – trend prediction is very important to an investor of cryptocurrency especially when a crypto seems to be unstable. Neural network are very helpful in forecasting the trend of the crypto.
- In showing a potential crypto – an investor is always on the look to see which potential crypto to invest, and these networks are becoming very helpful to them.
Effects of Neural Networks on Marketing
Over the years especially in our millennial age, neural network robots have been used in medicine, business, engineering, finance, mining, tourism etc. They have turned out to be very crucial tools for various marketers in the following areas:
- In predicting consumer behavior – this is an important factor for every business and marketer that determines their level of production or distribution. Neural networks allow every business to predict their customers’ demeanor.
- In marketing automation – this is the direction of every serious minded business where they don’t lay the entire burden of sending and receiving mails, texts, calls and alerts on a person. They automate their activities using neural networks.
- Sales forecasting – by knowing the current and past behavior of the market, neural networks are able to predict what is expected in the future. This enables the business to have enough supply to cater for the demand to come and vice versa.
- In understanding buyer segments – in complex buyer segments, you need neural networks to give a thorough understanding of the segment. It will enable you know the population and the strategy to use to reach the market.
- Creating content – a business that has an online visibility needs quality content as a part of optimization. Neural networks are helping much in the area.
- In creating buyer segments – complicated buyer segments have been created by neural networks that would be hard for a man to do.
Some platforms, like DialogTech, use neural networks to classify inbound calls into predetermined categories or to assign a lead quality score to calls. The neural network performs these actions based on the call transcriptions and the marketing channel or keyword that drove the call. For example, a caller who is speaking with a dental office may ask to schedule an appointment. The neural network will seek, find and classify that phrase as a conversation, therefore providing marketers with valuable insights into the performance of marketing initiatives.
Neural networks have advanced from very simple modes to highly complex learning simulations. They are in our phones, tablets, and running many of the web services we use. There are many other machine learning systems out there. But because of their similarity to the human brain, these robots are some of the most fascinating technologies.
As we continue to develop and refine models, there is no better description of their capabilities. If performance results on the cryptocurrency market should be taken with great care, since the market is constantly experiencing abnormal positive returns, neural network robots especially RoFx, are useful tools for trend predictions by achieving high prediction accuracy.
Nevertheless, sometimes prediction of the market performance can be risky. That’s why an investment system should take into account all the factors within its environment during its implementation. It is interesting to study how such strategy would perform by adding a financial policy and risk measure to learning process in a reinforcement learning manner. This is because neural networks may not accurately predict especially when there are misclassifications.