The developers always try to relate the technology with the real world i.e the artificial neural networks have been created by studying the human brain that how we as humans take input and process the information in our brain and resembling the neurons( which are the basic unit of our brain) the tech guys created the artificial neural networks which helps the machine to take accurate decisions.
Neural networks are a set of algorithms which are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data. As our brain is capable of quickly assessing and understanding the context of numerous different situations, computers struggle to react to situations in a similar way. So, Artificial Neural Networks are a way of overcoming this limitation.
First developed in 1940s Artificial Neural Networks attempt to simulate the way the brain operates. Sometimes called perceptrons, an Artificial Neural Network is a hardware or software system. Some networks are a combination of the two. Consisting of a network of layers this system is patterned to replicate the way the neurons in the brain operate.
The network comprises an input layer, where data is entered, and an output layer. The output layer is where processed information is presented. Connecting the two is a hidden layer or layers. The hidden layers consist of units that transform input data into useful information for the output layer to present. In addition to replicating the human decision making progress Artificial Neural Networks allow computers to learn. Their structure also allows ANN’s to reliably and quickly identify patterns that are too complex for humans to identify. Artificial Neural Networks also allow us to classify and cluster large amounts of data quickly.
Artificial neural networks are a form of deep learning. They are also one of the main tools used in machine learning. Consequently ANN’s play an increasingly important role in the development of artificial intelligence. The rise in importance of Artificial Neural Network’s is due to the development of “backpropagation”. This technique allows the system’s hidden layers to become versatile. Adapting to situations where the outcome doesn’t match the one originally intended. The development of deep learning neural networks has also helped in the development of Artificial Neural Networks. Deep learning neural networks are networks made up of multiple layers. This allows the system to become more versatile. Different layers are able to analyse and extract different features. This process allows the system to identify new data or images. It also allows for unsupervised learning and more complex tasks to be undertaken.
As we have seen Artificial Neural Networks are made up of a number of different layers. Each layer houses artificial neurons called units. These artificial neurons allow the layers to process, categorize, and sort information. Alongside the layers are processing nodes. Each node has its own specific piece of knowledge. This knowledge includes the rules that the system was originally programmed with. It also includes any rules the system has learned for itself. This makeup allows the network to learn and react to both structured and unstructured information and data sets.
Almost all artificial neural networks are fully connected throughout these layers. Each connection is weighted. The heavier the weight, or the higher the number, the greater the influence that the unit has on another unit. The first layer is the input layer. This takes on the information in various forms. This information then progresses through the hidden layers where it is analysed and processed. By processing data in this way, the network learns more and more about the information. Eventually, the data reaches the end of the network, the output layer. Here the network works out how to respond to the input data. This response is based on the information it has learned throughout the process. Here the processing nodes allow the information to be presented in a useful way.
Artificial Neural Networks can classify information, cluster data, or predict outcomes. They can be used for a range of tasks. These include analyzing data, transcribing speech into text, powering facial recognition software, or predicting the weather.
- By adopting Artificial Neural Networks businesses are able to optimize their marketing strategy. This includes customers personal details, shopping patterns as well as any other information relevant to your business. Once processed this information can be sorted and presented in a useful and accessible way. This is generally known as market segmentation. Businesses can identify and target customers most likely to purchase a specific service or produce. This focusing of marketing campaigns means that time and expense isn’t wasted advertising to customers who are unlikely to engage. This application of Artificial Neural Networks can save businesses both time and money. It can also help to increase profits.
- Developing Targeted Marketing Campaigns Through unsupervised learning, Artificial Neural Networks are able to identify customers with a similar characteristic. This allows businesses to group together customers with similarities, such as economic status or preferring vinyl records to downloaded music.
- Reducing Email Fatigue and Improving Conversion Rates by only advertising relevant products to interested customers, you also reduce the chances of customers developing email fatigue. According to dragon360.com 61% of customers say that they are most likely to use companies that send them targeted content.
- Improving Search Engine Functionality
- Artificial Neural Networks are being used by the pharmaceutical industry in a number of ways. The most obvious application is in the field of disease identification and diagnosis. It was reported in 2015 that in America 800 possible cancer treatments were in the trial. With so much data being produced, Artificial Neural Networks are being used to help scientists efficiently analyze and interpret it.
- The network models analyze location, historical data sets, as well as weather forecasts, models and other pieces of relevant information.
- By predicting a potential rise in demand the company is able to increase stock in store. This means that customers won’t leave empty-handed and also allows Walmart to offer product-related offers and incentives.
This is all from my side. Thank you for reading!!:)
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